diff --git a/data/images/nn.png b/data/images/nn.png
new file mode 100644
index 0000000..f0c6241
Binary files /dev/null and b/data/images/nn.png differ
diff --git a/data/results/blueleg_beam_cube.csv b/data/results/blueleg_beam_cube.csv
deleted file mode 100644
index ae28ac6..0000000
--- a/data/results/blueleg_beam_cube.csv
+++ /dev/null
@@ -1,1340 +0,0 @@
-# extended ;1
-# legs ;['blueleg']
-# legs model ;['beam']
-# legs young modulus ;[35000.]
-# legs poisson ratio ;[0.45]
-# legs position on motor ;['counterclockwisedown', 'clockwisedown', 'counterclockwisedown', 'clockwisedown']
-# connector ;bluepart
-# connector type ;rigid
-Effector position;Motor angle
-[-39.96175515 -90.41789743 -39.96175525];[-0.14670205865712832, 0.14670207392254797, 2.43823807942873, -2.438238056118855]
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-[-39.95397373 -90.45505537 -23.95436099];[0.4217800556565714, 0.13599500085968805, 2.113863614076125, -2.5101582582004642]
-[-39.9514583  -90.46332739 -15.96017202];[0.7233308263521361, 0.14326494422921077, 1.8979428979904553, -2.5098560718291005]
-[-39.95029449 -90.46182801  -7.97640971];[1.0359369002803307, 0.15246389567464924, 1.640800631571854, -2.4992699487352867]
-[-3.99504339e+01 -9.04556845e+01 -8.41130293e-05];[1.3485569542409783, 0.1566254899703859, 1.3478513803217718, -2.4934454150763674]
-[-39.95030199 -90.46184336   7.97624499];[1.6414899527742801, 0.15240236705143834, 1.0352442665202963, -2.4993421303767764]
-[-39.95147414 -90.46332964  15.96003069];[1.8985676511956462, 0.14314151556805438, 0.7226847834162188, -2.5099569457034727]
-[-39.95399528 -90.45504438  23.95425688];[2.1143579292249797, 0.13582149319431325, 0.4212226262267405, -2.5102245091695705]
-[-39.95722698 -90.44149066  31.95602923];[2.291985529137703, 0.13605125127358142, 0.13255056789712438, -2.4880598503804885]
-[-39.96176728 -90.41790677  39.96172563];[2.4383448096680334, 0.14662262180203295, -0.1468750801159724, -2.438154889316028]
-[-31.95609878 -90.44150247  39.95720194];[2.488096868108306, -0.13298479557014634, -0.1362470344030639, -2.291649677921424]
-[-31.95073847 -90.47098949  31.95076903];[2.3391434520242362, -0.14668579139394952, 0.14689926428034492, -2.3392542486478054]
-[-31.94759232 -90.48698443  23.94883039];[2.1595613970021565, -0.14925905308243476, 0.43977545758627545, -2.3588026373706246]
-[-31.94555268 -90.49734691  15.9551842 ];[1.9416411005103975, -0.1431314244158328, 0.7463431701874284, -2.35575181827918]
-[-31.94504709 -90.49667716   7.97332306];[1.6813741752483664, -0.1341894257864426, 1.0649730942073528, -2.34271057353412]
-[-3.19455220e+01 -9.04904620e+01  5.26859336e-05];[1.3834427735166974, -0.1299947521187923, 1.3838456140737354, -2.335839587196695]
-[-31.94504717 -90.49669261  -7.9732199 ];[1.064563635518372, -0.13423122454140354, 1.6817521084168139, -2.34275567201657]
-[-31.94555685 -90.49734873 -15.95509713];[0.7459495910232063, -0.14321942262744727, 1.941965534458917, -2.3558077768679784]
-[-31.94760305 -90.48697131 -23.94876827];[0.4394259864822754, -0.14939126450210363, 2.1598000943484004, -2.358814723059503]
-[-31.95075624 -90.47099723 -31.95072381];[0.1466230678474144, -0.14684478079672178, 2.3392838231354856, -2.3391691395985994]
-[-31.95607966 -90.44150347 -39.95719542];[-0.13625079500236922, -0.13286072742098903, 2.488114144102542, -2.2917118101365626]
-[-23.95455802 -90.45308298 -39.95417686];[-0.13597190460893988, -0.4218161913924705, 2.510239466951983, -2.1139475110198216]
-[-23.94903    -90.48471072 -31.94785433];[0.14943008745589437, -0.4395819166448922, 2.3588934980846687, -2.1598350429679463]
-[-23.94469518 -90.51528517 -23.94471244];[0.4446235344943992, -0.4444867001219755, 2.177334351316207, -2.177389954585183]
-[-23.94288385 -90.52913518 -15.9514764 ];[0.7539919897288637, -0.43941574141841244, 1.9585003485000718, -2.1731361538915506]
-[-23.9427064  -90.53099801  -7.97098264];[1.0757155166669985, -0.4305703820323267, 1.6971783651849117, -2.159202999572743]
-[-2.39428086e+01 -9.05304377e+01 -2.73672314e-05];[1.3973083210824027, -0.42619789170642786, 1.3971141721258598, -2.151767055810742]
-[-23.94270363 -90.53100675   7.97092921];[1.6973504654239358, -0.4305938064491704, 1.075512085434463, -2.1592251985370865]
-[-23.94288289 -90.52913412  15.95143279];[1.958636646302441, -0.4394677633998252, 0.7537929000584022, -2.173152519262315]
-[-23.94470045 -90.51528141  23.94468118];[2.17742286038113, -0.444571573238719, 0.4444436345338839, -2.1773633907382735]
-[-23.9490444  -90.48472557  31.94782866];[2.358929437699089, -0.4396942411163303, 0.14928699712749732, -2.159731238546765]
-[-23.95452027 -90.45309246  39.95417406];[2.510275727380207, -0.4216046666401223, -0.1359331411656201, -2.114100453437638]
-[-15.96104819 -90.45303098  39.95255632];[2.5102803122138178, -0.723553970350095, -0.14314425865653901, -1.898393581810774]
-[-15.95615853 -90.48588654  31.94682447];[2.3562799236028256, -0.746357047131776, 0.1433617475368309, -1.9423668409917505]
-[-15.95240573 -90.5186599   23.9440185 ];[2.1736056752187216, -0.7540915410207774, 0.4396552007676067, -1.9590615737468304]
-[-15.94863845 -90.56032892  15.9486446 ];[1.9537046026566975, -0.7494569012464325, 0.749511946588348, -1.9537251942829381]
-[-15.94841077 -90.56684014   7.96913141];[1.6929794223938428, -0.7404285967598951, 1.0718286839494746, -1.940177869985488]
-[-1.59478651e+01 -9.05750210e+01  1.12956145e-05];[1.3931795369860156, -0.735667912705279, 1.3932556111979724, -1.9326719096438305]
-[-15.94840796 -90.56683845  -7.96911036];[1.0717462385636816, -0.7404390948188407, 1.6930403929557805, -1.9401888479010154]
-[-15.94863583 -90.56032251 -15.94862905];[0.7494328179131815, -0.749481012818518, 1.9537475317893513, -1.9537264535510213]
-[-15.95240763 -90.51866178 -23.94400737];[0.4395857522143031, -0.7541345100103509, 2.173630169834014, -1.9590283998547842]
-[-15.95616911 -90.48589381 -31.94681305];[0.14331099138053593, -0.7464201466762352, 2.356285062812222, -1.9422819121091244]
-[-15.96098791 -90.45305119 -39.95255514];[-0.1430923098305642, -0.7232652968535058, 2.510331092911661, -1.8986439397416706]
-[ -7.97731431 -90.44386977 -39.95226216];[-0.15226268806499577, -1.036438363796564, 2.499989133732867, -1.6415352303632345]
-[ -7.97432397 -90.47678265 -31.94727066];[0.13447849279833773, -1.0653125840266289, 2.3435751834984226, -1.6824282893557538]
-[ -7.97205445 -90.51061135 -23.94490782];[0.43092161835692105, -1.076178024581587, 2.1601047130112545, -1.6981397505277687]
-[ -7.96969917 -90.55609676 -15.94938152];[0.740669447348276, -1.0720792367211316, 1.9406478969036784, -1.6934577213843887]
-[ -7.96731463 -90.6069233   -7.96731613];[1.061363906701676, -1.061344934475184, 1.6795235212479058, -1.6795263229720756]
-[-7.96694608e+00 -9.06171798e+01 -2.50304629e-06];[1.3814206441162875, -1.0560902909750411, 1.3813960587272032, -1.6726905688357072]
-[ -7.96731265 -90.60691655   7.96731096];[1.6795378274922064, -1.0613510577303942, 1.0613345684998268, -1.6795348588461332]
-[ -7.96969649 -90.556093    15.94937966];[1.940654151721303, -1.0720893494104726, 0.7406446871248006, -1.693460492949137]
-[ -7.97205428 -90.51061329  23.94490708];[2.160109130639046, -1.0761931904059463, 0.4309049884795718, -1.6981210003677356]
-[ -7.97432777 -90.47678987  31.94726724];[2.3435798989792374, -1.0653352701149754, 0.13447305349877747, -1.682383379612519]
-[ -7.97723731 -90.44390006  39.95226013];[2.500027629506483, -1.036099017406245, -0.15222487498225784, -1.6418553985784439]
-[-8.12863421e-05 -9.04307133e+01  3.99531979e+01];[2.4944294742776725, -1.3494389535795874, -0.15635160710314946, -1.348721827579732]
-[-1.05686902e-06 -9.04628115e+01  3.19486220e+01];[2.3369897340867576, -1.3846410063750403, 0.1303508920555701, -1.3846392103329994]
-[ 3.30870119e-08 -9.05009008e+01  2.39459977e+01];[2.153037576371106, -1.3982760286325335, 0.4266676503956386, -1.3982763949803299]
-[-1.53753104e-08 -9.05538125e+01  1.59497759e+01];[1.9335815213207637, -1.3939677160610309, 0.7361040445961811, -1.3939676228009814]
-[-1.70640841e-08 -9.06058234e+01  7.96754697e+00];[1.6731433202136006, -1.3817967335676307, 1.0564015767024835, -1.3817966394527736]
-[-1.77624396e-08 -9.06648652e+01  6.77969033e-07];[1.3741623364302438, -1.374168284605788, 1.3741750858601645, -1.3741682410483194]
-[-1.63751846e-08 -9.06058160e+01 -7.96754638e+00];[1.0563856046092015, -1.3818040406275507, 1.6731457272961952, -1.3818039966344144]
-[-1.54143264e-08 -9.05538105e+01 -1.59497777e+01];[0.736092906104697, -1.3939737991788526, 1.93357793907693, -1.393973702537071]
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-[-1.24680461e-08 -9.04628121e+01 -3.19486222e+01];[0.13035642759501115, -1.3846324929407134, 2.3369957751767, -1.3846324001054733]
-[-1.04201836e-08 -9.04306828e+01 -3.99531998e+01];[-0.15634175716898982, -1.3490765493626435, 2.4944365206064223, -1.3490765183030322]
-[  7.97715276 -90.443921   -39.95226483];[-0.15219865549506173, -1.6422313751973525, 2.5000613213438454, -1.0357363879264512]
-[  7.97432205 -90.4767884  -31.9472694 ];[0.13448093916341936, -1.6824260569426805, 2.343575447761615, -1.0653080236459953]
-[  7.97205448 -90.51061147 -23.9449078 ];[0.43092156168717743, -1.698139446965433, 2.1601046651903237, -1.076178256546536]
-[  7.96969911 -90.55609667 -15.94938152];[0.7406694413165817, -1.693457851353887, 1.9406479068376516, -1.0720790489414191]
-[  7.96731456 -90.60692334  -7.96731613];[1.0613639123527263, -1.6795264489981139, 1.6795235179757986, -1.061344748481447]
-[ 7.96694602e+00 -9.06171796e+01 -3.02260465e-06];[1.3814210505004023, -1.672690761712592, 1.3813956508557337, -1.0560900635268893]
-[  7.96731257 -90.60691653   7.96731096];[1.679537832219701, -1.6795350458497011, 1.0613345684939084, -1.061350829915886]
-[  7.96969642 -90.55609295  15.94937967];[1.9406541537971884, -1.6934606174152511, 0.740644681289429, -1.0720891219868018]
-[  7.97205423 -90.51061324  23.94490708];[2.160109157272046, -1.6981211259097009, 0.43090498660552023, -1.076193035481303]
-[  7.97432771 -90.47678984  31.94726724];[2.343579920639852, -1.6823834964932467, 0.13447304443368463, -1.0653350733394837]
-[  7.97723725 -90.44390013  39.95226012];[2.50002764263836, -1.641855566677937, -0.1522248916329213, -1.0360988223272514]
-[ 15.96090731 -90.45306477  39.95256822];[2.5103860484537424, -1.8990211468042284, -0.1430172836284259, -0.7229022595231435]
-[ 15.95615779 -90.48589064  31.94682286];[2.356279630944627, -1.9423609466822254, 0.14336597929266665, -0.7463515424009959]
-[ 15.95240575 -90.51866008  23.94401848];[2.17360562689577, -1.959061349967301, 0.4396551734089982, -0.7540917803762949]
-[ 15.94863841 -90.56032882  15.94864461];[1.9537046606392496, -1.9537253562399393, 0.7495119755186722, -0.7494567661418343]
-[ 15.94841072 -90.56684016   7.96913141];[1.6929794480694573, -1.9401780210808353, 1.0718287202215173, -0.7404284526811237]
-[ 1.59478650e+01 -9.05750211e+01  1.12959438e-05];[1.3931795973699868, -1.932672057112956, 1.3932556439459387, -0.7356678058411985]
-[ 15.94840791 -90.56683845  -7.96911036];[1.0717462791531083, -1.9401889356273148, 1.69304043431371, -0.7404389844830127]
-[ 15.94863578 -90.56032255 -15.94862905];[0.7494328525676606, -1.9537265886306263, 1.9537475554100086, -0.7494808498336782]
-[ 15.95240757 -90.51866191 -23.94400735];[0.4395857771494194, -1.9590284923907582, 2.1736302007454746, -0.754134343438744]
-[ 15.95616906 -90.48589385 -31.94681305];[0.14331100120155205, -1.9422820152340696, 2.356285102000696, -0.7464199963949243]
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-[ 23.9544523  -90.45309591 -39.95419613];[-0.13579656189794664, -2.1144450512759363, 2.5103071281086873, -0.4212574020581665]
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-[ 23.94469522 -90.51528506 -23.94471244];[0.4446235092272229, -2.1773898075075118, 2.177334366506258, -0.44448691069292456]
-[ 23.94288382 -90.52913516 -15.95147641];[0.7539920462196517, -2.1731362102538068, 1.9585004180058685, -0.43941566184098413]
-[ 23.94270637 -90.53099808  -7.97098264];[1.0757155762673052, -2.1592031141919588, 1.69717842417094, -0.4305703114526773]
-[ 2.39428086e+01 -9.05304378e+01 -2.85247080e-05];[1.3973076774029414, -2.1517671615478418, 1.3971149818786344, -0.426197769434314]
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-[ 23.94470044 -90.51528128  23.94468119];[2.1774229275546895, -2.177363468158311, 0.4444436927474532, -0.4445715181609486]
-[ 23.94904437 -90.48472564  31.94782865];[2.358929491709064, -2.1597313215352427, 0.14928702774471414, -0.43969418237058705]
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-[ 31.94555267 -90.497347    15.9551842 ];[1.9416411625522767, -2.355751832935112, 0.7463432314856134, -0.1431313986046816]
-[ 31.94504709 -90.49667715   7.97332306];[1.6813742560058, -2.342710599917993, 1.0649731713077133, -0.13418940398770476]
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-[ 31.94504717 -90.49669262  -7.9732199 ];[1.0645636821365032, -2.342755747053389, 1.6817521720508088, -0.1342312036500603]
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-[ 31.95075622 -90.47099736 -31.9507238 ];[0.14662313038905558, -2.3391691464814426, 2.339283862865947, -0.14684476709619904]
-[ 31.95607966 -90.44150345 -39.95719543];[-0.13625074080525312, -2.2917118495363438, 2.4881142090783195, -0.13286071642663255]
-[ 39.96172464 -90.41790802 -39.96176812];[-0.146625725590601, -2.438347057312435, 2.4381554199465585, 0.14687120229803277]
-[ 39.95720149 -90.44150386 -31.9560989 ];[0.1329836889920854, -2.48809687761266, 2.291650085789081, 0.1362462010565399]
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-[ 39.9514583  -90.46332753 -15.960172  ];[0.7233308742187391, -2.5098560669687795, 1.8979429634360554, 0.14326485114666068]
-[ 39.95029451 -90.46182799  -7.97640971];[1.0359369487637546, -2.4992699431239593, 1.640800704962473, 0.15246381121941607]
-[ 3.99504339e+01 -9.04556846e+01 -8.41106934e-05];[1.3485570037939125, -2.4934454568643645, 1.347851451167933, 0.15662539215505428]
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-[ 2.36365566e+01 -1.69460642e+02  2.03185789e-05];[-0.6911504815519017, 0.06412534296867133, -0.691064779184457, 1.229176271059877]
-[  23.62884536 -169.43264602    7.90098958];[-0.5142388391894887, 0.07241887012656573, -0.8971361049701977, 1.242521312163083]
-[  23.55328335 -169.23676297   15.72739052];[-0.3381172161468806, 0.11157177944521299, -1.122384967593379, 1.3143518538834533]
-[  23.09034965 -168.26370622   23.11502043];[-0.1280731425513637, 0.17819870798074908, -1.24818805785431, 1.4000034559828256]
-[  22.06939267 -165.89895976   29.38868285];[0.08318788780523831, 0.2235812850813556, -1.3281195688146896, 1.4121322318502822]
-[  21.18607634 -163.42876212   35.06620343];[0.26253722757092385, 0.26216348044573556, -1.4443352449023201, 1.412132231850282]
-[  27.11954193 -161.53655      34.16773821];[0.22908265721397195, 0.04886390663660904, -1.4443352449023192, 1.4812295391658168]
-[  28.35926075 -163.83038825   28.56385877];[0.03244435773072658, -0.002062528511584394, -1.378088184977595, 1.4812295391658172]
-[  29.3410825  -165.90156407   22.14176816];[-0.1755236060877878, -0.04176787877649116, -1.2694735397215249, 1.4812295391658172]
-[  30.29144643 -167.61071184   15.25271322];[-0.4081329047313612, -0.07810638297657724, -1.1782346412960054, 1.4812295391658177]
-[  30.8767989  -168.54687603    7.77380147];[-0.6320705749273767, -0.10226896418169487, -1.0314452540285304, 1.4812295391658177]
-[ 3.10012245e+01 -1.68743134e+02 -1.79643467e-04];[-0.8326107936328132, -0.10990685316546206, -0.8328071942285551, 1.4812295391658177]
-[  30.87720858 -168.54751476   -7.77416118];[-1.0315580898448926, -0.10226825590151227, -0.6324108727378468, 1.4812295391658177]
-[  30.38483863 -167.74285033  -15.32148505];[-1.2089788474735748, -0.07846497857886911, -0.4215405051612965, 1.4909154818672983]
-[  29.44977678 -166.06865926  -22.25476685];[-1.319368455679348, -0.04258070057178813, -0.19231404047114978, 1.4909154818672983]
-[  28.30007605 -163.74465114  -28.25380559];[-1.3193684556793486, 0.0012464273999104325, 0.0425764922274339, 1.4909154818672987]
-[  27.35831562 -161.50722554  -33.80485605];[-1.4087469075246628, 0.040361659389007806, 0.22689994539687103, 1.4909154818672987]
-[  32.38209682 -159.39123722  -33.02452002];[-1.4087469075246624, -0.15190020589961706, 0.1989561079692092, 1.5047226258115032]
-[  33.89565714 -161.31230358  -27.73973211];[-1.3858180572266052, -0.20641762642156364, -0.0011222774775029448, 1.5319199988056873]
-[  34.7908826  -163.09680632  -21.26469037];[-1.2568232362963132, -0.2439724095956537, -0.20415118081359754, 1.5319199988056873]
-[  35.74900617 -164.70808223  -14.53370168];[-1.195313885905933, -0.2750922152703228, -0.4469576572292179, 1.552437201981196]
-[  36.46986558 -165.81056232   -7.37358694];[-1.104946417247003, -0.30083941712505263, -0.7066061390634325, 1.5524372019811965]
-[ 3.67840453e+01 -1.66273032e+02 -3.65439603e-03];[-0.9501954038397933, -0.31037756082567675, -0.9499773353447922, 1.5524372019811965]
-[  36.49921894 -165.85163656    7.36990736];[-0.7142232836235356, -0.3018095790579539, -1.1148006554827892, 1.5524372019811967]
-[  35.8102536  -164.80261022   14.50371243];[-0.4604145834390506, -0.2770356221876716, -1.217590287267636, 1.5524372019811965]
-[  34.82440736 -163.15897508   20.99858091];[-0.21012764329436973, -0.24029775690906463, -1.2426010201747857, 1.5524372019811965]
-[  33.90000109 -161.40748015   27.06960753];[-0.006691575576233889, -0.20335827657528718, -1.3242669057980814, 1.5524372019811965]
-[  32.87051281 -159.26370075   32.32392185];[0.19451145138082615, -0.1628088382820468, -1.3425661002318308, 1.5524372019811965]
diff --git a/data/results/blueleg_beam_cube1331.csv b/data/results/blueleg_beam_cube1331.csv
new file mode 100644
index 0000000..4d842b6
--- /dev/null
+++ b/data/results/blueleg_beam_cube1331.csv
@@ -0,0 +1,1340 @@
+# extended ;1
+# legs ;['blueleg']
+# legs model ;['beam']
+# legs young modulus ;[35000.]
+# legs poisson ratio ;[0.45]
+# legs position on motor ;['counterclockwisedown', 'clockwisedown', 'counterclockwisedown', 'clockwisedown']
+# connector ;bluepart
+# connector type ;rigid
+Effector position;Motor angle
+[-39.96175319 -90.41790123 -39.9617533 ];[-0.14671493815230305, 0.14671495863621398, 2.438245119379324, -2.438245064719189]
+[-39.95721404 -90.44149463 -31.95606774];[0.13278020542251584, 0.1361462088456899, 2.291810947074616, -2.4880786839009525]
+[-39.95398396 -90.45504974 -23.95431237];[0.42151032888874346, 0.13590327750354508, 2.114101371561853, -2.5101908380204403]
+[-39.9514658  -90.46332978 -15.96010252];[0.7230089875813547, 0.14319760751651572, 1.8982513705899897, -2.5099090246584557]
+[-39.95029801 -90.4618368   -7.97632692];[1.0355881112380285, 0.15242992354764623, 1.6411482937976718, -2.499306559350879]
+[-3.99504366e+01 -9.04556592e+01 -3.71598144e-09];[1.3482063788410472, 0.1566237350843377, 1.3482063413733782, -2.4934475678505583]
+[-39.95029798 -90.46183701   7.9763269 ];[1.6411483233881732, 0.1524299215591997, 1.0355880724518913, -2.499306550733385]
+[-39.9514658  -90.46332981  15.96010251];[1.8982513919169168, 0.1431976063501493, 0.7230089551400256, -2.509909030614221]
+[-39.95398395 -90.45504983  23.95431237];[2.114101370650205, 0.13590326506085199, 0.4215103419337676, -2.510190839017309]
+[-39.95721403 -90.4414947   31.95606774];[2.291810946563265, 0.1361461968015335, 0.13278021635528078, -2.488078667982854]
+[-39.96175319 -90.41790124  39.9617533 ];[2.438245117769097, 0.1467149488359607, -0.14671492986357987, -2.4382450565310547]
+[-31.95606778 -90.44149348  39.95721427];[2.488078779281501, -0.13278021903656137, -0.13614616867612922, -2.291810968604452]
+[-31.95074983 -90.47098055  31.95074992];[2.339207542918932, -0.14677073146858444, 0.14677074432874765, -2.3392075180725564]
+[-31.94759784 -90.48697768  23.94880222];[2.1596727262205526, -0.14933234601435222, 0.4396062809896361, -2.358806163917552]
+[-31.94555441 -90.49734877  15.95514217];[1.9417992725549769, -0.14318142670222175, 0.7461472116764456, -2.3557791473372935]
+[-31.94504669 -90.49668602   7.97327149];[1.6815637171793152, -0.1342136540721711, 1.0647670100381232, -2.342733397863045]
+[-3.19455240e+01 -9.04904408e+01  2.83381468e-09];[1.383645954832629, -0.12999660300079946, 1.3836459616090193, -2.3358416103844735]
+[-31.94504669 -90.49668599  -7.97327148];[1.0647669834967672, -0.13421364567647373, 1.681563717215456, -2.3427334048954926]
+[-31.94555441 -90.49734875 -15.95514218];[0.7461472315677953, -0.14318144475837905, 1.9417992706247438, -2.355779142833153]
+[-31.94759783 -90.48697775 -23.94880222];[0.439606291486437, -0.14933235214329219, 2.159672705537436, -2.3588061562192024]
+[-31.95074983 -90.4709805  -31.95074993];[0.14677073223347042, -0.14677070850728594, 2.339207546181028, -2.3392075283667255]
+[-31.95606779 -90.44149344 -39.95721427];[-0.13614616016925346, -0.13278022193104544, 2.4880787683988563, -2.2918109586598154]
+[-23.95450848 -90.45308854 -39.95418569];[-0.13587910839852535, -0.42154337102627215, 2.5102736721140886, -2.1141887564460795]
+[-23.94903903 -90.48471776 -31.94784274];[0.1493642897863678, -0.4396446799113153, 2.3589065186849667, -2.1597755684304714]
+[-23.94469921 -90.51527932 -23.94469928];[0.4445368338516936, -0.4445368173075581, 2.1773720074265426, -2.1773720077232186]
+[-23.94288347 -90.52913531 -15.95145598];[0.7538933827683721, -0.439447255529148, 1.9585641244393384, -2.1731422099172106]
+[-23.9427047  -90.53100328  -7.97095623];[1.0756132688414803, -0.4305851944021051, 1.6972631992737532, -2.1592140302129894]
+[-2.39428078e+01 -9.05304435e+01 -2.43050700e-09];[1.3972106945788945, -0.42619962423652946, 1.3972106692672843, -2.1517672729271253]
+[-23.94270472 -90.53100318   7.97095623];[1.697263224520423, -0.4305852342954352, 1.0756132834143668, -2.1592140509057907]
+[-23.94288347 -90.52913529  15.95145599];[1.958564135530507, -0.4394472906263014, 0.7538933858773061, -2.1731422187141094]
+[-23.94469921 -90.51527929  23.94469928];[2.1773720153695866, -0.4445368165931736, 0.4445368202369803, -2.177372009474179]
+[-23.94903901 -90.48471798  31.94784271];[2.358906524344768, -0.43964467193315276, 0.14936427234376645, -2.1597755531233265]
+[-23.95450848 -90.45308849  39.9541857 ];[2.5102736766406215, -0.42154336554072314, -0.13587910142550325, -2.11418875700068]
+[-15.96097876 -90.45304864  39.95256183];[2.51033513669127, -0.7232274664748326, -0.14307524307722344, -1.898705891912382]
+[-15.95616536 -90.48589073  31.94681911];[2.3562796585775323, -0.7463937431961944, 0.143339540458171, -1.9423172818028558]
+[-15.95240806 -90.51866088  23.94401391];[2.173613945522759, -0.7541191861005825, 0.4396221119331077, -1.9590400829760835]
+[-15.94863761 -90.56032673  15.94863766];[1.9537230213033683, -0.7494730536222536, 0.7494730484399393, -1.953723019599933]
+[-15.94840937 -90.56683945   7.9691212 ];[1.6930084247524568, -0.7404362872017168, 1.0717872991213864, -1.9401828748016927]
+[-1.59478647e+01 -9.05750242e+01  2.67690195e-09];[1.393216844314951, -0.7356694479990675, 1.3932168563179643, -1.9326720718605026]
+[-15.94840936 -90.56683949  -7.9691212 ];[1.0717872863648452, -0.7404362787453058, 1.693008427604692, -1.9401828716122511]
+[-15.94863761 -90.56032678 -15.94863766];[0.7494730483714571, -0.7494730263232823, 1.9537230123690028, -1.9537230166445114]
+[-15.95240807 -90.51866078 -23.94401392];[0.4396221247499229, -0.754119191453702, 2.1736139503860357, -1.9590400945796298]
+[-15.95616535 -90.48589086 -31.94681909];[0.1433395578083672, -0.7463937753340645, 2.35627961715335, -1.942317268864989]
+[-15.96097877 -90.45304846 -39.95256185];[-0.14307524735365093, -0.7232274882591172, 2.5103351404508403, -1.898705896150348]
+[ -7.97723298 -90.44389637 -39.95226346];[-0.15222724055667344, -1.0360839055735758, 2.500026760735235, -1.6418868350580853]
+[ -7.97432643 -90.47678749 -31.94726877];[0.134477085721213, -1.0653264057297476, 2.3435765212815216, -1.68240221439372]
+[ -7.97205533 -90.51061211 -23.94490767];[0.43091414898307945, -1.0761886623825645, 2.1601059278951324, -1.6981271454960412]
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+[ -7.97205532 -90.51061217  23.94490766];[2.1601059359019827, -1.0761886567007268, 0.43091411120592266, -1.6981271387270134]
+[ -7.97432643 -90.4767875   31.94726877];[2.3435765301278413, -1.0653264112733054, 0.1344770699067997, -1.682402212296529]
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+[-1.14798194e-09 -9.04306781e+01  3.99532019e+01];[2.494433080158018, -1.3490826642117992, -0.15634941015330223, -1.3490826359481283]
+[-1.56896395e-09 -9.04628103e+01  3.19486222e+01];[2.336992478145063, -1.384636595421805, 0.1303539140226962, -1.3846365490287071]
+[-1.22865416e-09 -9.05009011e+01  2.39459988e+01];[2.153038136577002, -1.398275286339044, 0.4266667767619355, -1.3982752367921765]
+[-8.35991083e-10 -9.05538123e+01  1.59497769e+01];[1.9335800079051988, -1.3939702909792533, 0.7360990187002876, -1.3939702482568486]
+[-5.84480392e-10 -9.06058208e+01  7.96754668e+00];[1.673144450363205, -1.3818005166374447, 1.0563936881277316, -1.3818004857095583]
+[-4.80980839e-10 -9.06648691e+01 -1.85676981e-10];[1.374168249824078, -1.374168274946915, 1.3741682480726172, -1.3741682482906692]
+[-2.92233049e-10 -9.06058208e+01 -7.96754668e+00];[1.0563936735795942, -1.3818005072040538, 1.673144446128886, -1.3818004906478456]
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diff --git a/data/results/blueleg_beam_sphere.csv b/data/results/blueleg_beam_sphere.csv
deleted file mode 100644
index faef05d..0000000
--- a/data/results/blueleg_beam_sphere.csv
+++ /dev/null
@@ -1,524 +0,0 @@
-# extended ;1
-# legs ;['blueleg']
-# legs model ;['beam']
-# legs young modulus ;[35000.]
-# legs poisson ratio ;[0.45]
-# legs position on motor ;['counterclockwisedown', 'clockwisedown', 'counterclockwisedown', 'clockwisedown']
-# connector ;bluepart
-# connector type ;rigid
-Effector position;Motor angle
-[-1.25021896e-13 -9.06648691e+01  1.35906610e-10];[1.3741682595051454, -1.374168284375867, 1.3741682588178878, -1.3741682843758771]
-[ 2.39125979e+01 -9.84954169e+01 -1.11072037e-10];[1.1265506628710156, -1.8434176839407774, 1.1265506585724225, -0.2882980292322572]
-[ 15.95617193 -98.49018309 -15.95629641];[0.5653233162876317, -1.6418778110571253, 1.6429984422137864, -0.5629344797887431]
-[ 15.95467287 -98.50002404  -7.97507551];[0.8494524037555735, -1.6384294148380847, 1.3998149385141447, -0.5599381527033831]
-[ 1.59536156e+01 -9.85093816e+01 -4.47172434e-07];[1.1317779403767718, -1.6353464642777964, 1.1317647035931613, -0.5575573976283918]
-[ 15.95467318 -98.50003482   7.97507213];[1.399826877145062, -1.6384269713538078, 0.8494407651149284, -0.5599345506028971]
-[ 15.95619934 -98.48973851  15.95619569];[1.6427464960765812, -1.6427388923090187, 0.564013359784287, -0.5640131713752644]
-[  7.97554694 -98.49052824  15.95553499];[1.6387918445824747, -1.4001569781634717, 0.5601120378991069, -0.8496857347668965]
-[  7.9731155  -98.53373544   7.97311577];[1.3950294284864537, -1.395030877784766, 0.8451554839526061, -0.8451551396201741]
-[ 7.97252889e+00 -9.85436507e+01 -1.28244136e-06];[1.1273789684288418, -1.3921757490104867, 1.127382110828358, -0.842731303272205]
-[  7.97311652 -98.53373503  -7.9731159 ];[0.8451542065124709, -1.3950288455644013, 1.395032134638541, -0.8451520350723972]
-[  7.97554637 -98.49052423 -15.95553448];[0.5601130591764254, -1.400161293678896, 1.638791938483803, -0.84967692894765]
-[ 1.50345979e-06 -9.84541965e+01 -2.39461456e+01];[0.27673438869804706, -1.130265001371229, 1.8509215485210437, -1.1303019731759283]
-[ 4.41657988e-09 -9.84905811e+01 -1.59553306e+01];[0.5578953002539006, -1.1323492042599237, 1.6360572314427497, -1.1323492501945414]
-[ 9.77462792e-10 -9.85336762e+01 -7.97303075e+00];[0.8429658011787454, -1.1276790751899761, 1.3925215178120016, -1.1276790975985371]
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-[ 6.34049789e-10 -9.85336682e+01  7.97303215e+00];[1.3925220596556867, -1.127676425553358, 0.8429674879046682, -1.1276764443850562]
-[ 5.71904753e-10 -9.84905690e+01  1.59553306e+01];[1.6360547720114975, -1.1323471384019694, 0.5578996929060104, -1.1323471572585864]
-[ 5.31728352e-10 -9.84542296e+01  2.39461494e+01];[1.8509038150060195, -1.13030011599976, 0.2767152693568087, -1.130300134914437]
-[ -7.97553868 -98.49057677  15.95553867];[1.6387660769052226, -0.8496906788794789, 0.5600860982543164, -1.4001921159918196]
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-[ -7.97311653 -98.53373498  -7.9731159 ];[0.8451541950407825, -0.8451520100978875, 1.3950321472761644, -1.3950288618398927]
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-[ -23.9418976  -106.42339782  -15.95990996];[0.3909712544120528, -0.1369522002674235, 1.358513538705798, -1.5742807771095337]
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-[  -7.95169978 -161.89084939   -7.95170103];[-0.547543594710566, 0.5475476391146673, -0.1968582742726685, 0.19684978694506428]
-[  -7.94925218 -161.86654488  -15.89360772];[-0.7470631147192234, 0.5676295574556589, -0.03772867772161351, 0.216839116704648]
-[ -15.8899724  -161.84155303  -15.88997252];[-0.7685697711216836, 0.7685692505029711, -0.05842724918625044, 0.058436026803069634]
-[ -15.89207506 -161.86464393   -7.94851921];[-0.5675679011418402, 0.7469751471790946, -0.21678833629316652, 0.03769990386675513]
-[-1.58915371e+01 -1.61870001e+02  1.23966199e-06];[-0.3853206601067901, 0.7401754747278609, -0.3852998097403327, 0.031174721006095897]
-[ -15.89207588 -161.86464432    7.94852136];[-0.21681111221120863, 0.7469695065407839, -0.5675504495381862, 0.03770283660611247]
-[ -15.88997229 -161.84155258   15.889972  ];[-0.058436093750048386, 0.768569723713873, -0.7685690355575892, 0.0584273482548823]
-[-2.38056441e+01 -1.61814817e+02 -1.22610272e-04];[-0.42261062856657616, 0.9641629944638216, -0.42396156277283914, -0.12187364597798109]
-[-2.30966517e-02 -1.69757385e+02 -1.70545311e-07];[-0.5694635144436553, 0.5752950339224056, -0.5694634630594128, 0.5634260489752585]
diff --git a/data/results/blueleg_beam_sphere515.csv b/data/results/blueleg_beam_sphere515.csv
new file mode 100644
index 0000000..1dafe9c
--- /dev/null
+++ b/data/results/blueleg_beam_sphere515.csv
@@ -0,0 +1,524 @@
+# extended ;1
+# legs ;['blueleg']
+# legs model ;['beam']
+# legs young modulus ;[35000.]
+# legs poisson ratio ;[0.45]
+# legs position on motor ;['counterclockwisedown', 'clockwisedown', 'counterclockwisedown', 'clockwisedown']
+# connector ;bluepart
+# connector type ;rigid
+Effector position;Motor angle
+[-5.77320380e-10 -9.06648691e+01 -1.62415454e-10];[1.3741682657227816, -1.3741682874012078, 1.3741682567130564, -1.374168255514899]
+[ 2.39429406e+01 -9.84811032e+01 -2.94787835e-10];[1.1294620766864052, -1.8498635650124466, 1.1294620696612285, -0.27633724265620485]
+[ 15.95619839 -98.4897368  -15.95619844];[0.5640166728322394, -1.6427420803056818, 1.6427420584424781, -0.5640166567782254]
+[ 15.95467292 -98.50002905  -7.97507383];[0.8494464717220014, -1.6384287369371628, 1.3998209001775819, -0.5599369813637924]
+[ 1.59536155e+01 -9.85093822e+01 -1.02740037e-09];[1.131771434790632, -1.6353467417797347, 1.1317714286345018, -0.5575576219077296]
+[ 15.95467292 -98.50002906   7.97507383];[1.3998209005863143, -1.6384287256981032, 0.8494464655312559, -0.5599369746079415]
+[ 15.95619839 -98.48973684  15.95619844];[1.6427420571037068, -1.6427420804402157, 0.564016664942112, -0.5640166565041816]
+[  7.97554524 -98.49053237  15.95553497];[1.6387914783266495, -1.4001628994674786, 0.5601111294148708, -0.8496796088500554]
+[  7.9731158  -98.53373546   7.97311583];[1.395030577881612, -1.3950306125929717, 0.8451543505881125, -0.845154341441914]
+[ 7.97252874e+00 -9.85436504e+01  1.85449082e-09];[1.1273804631731763, -1.392176273337603, 1.127380474340366, -0.8427317368683975]
+[  7.9731158  -98.5337355   -7.97311583];[0.845154348068954, -1.3950306181580538, 1.395030571059124, -0.8451543336058704]
+[  7.97554525 -98.49053233 -15.95553497];[0.5601111437225897, -1.4001629088648606, 1.638791498367142, -0.8496796194865488]
+[ 3.22239156e-09 -9.84542342e+01 -2.39461497e+01];[0.2767094401748295, -1.1303032598917575, 1.850905518674569, -1.1303032669632982]
+[ 2.25533632e-10 -9.84905768e+01 -1.59553308e+01];[0.5578964890707495, -1.1323491578242044, 1.6360554395533045, -1.1323491400092025]
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+[-8.23343430e-10 -9.85860968e+01 -1.60161513e-09];[1.1238396469060712, -1.1238396702646134, 1.1238396384661757, -1.123839646918729]
+[-9.54923939e-10 -9.85336733e+01  7.97303140e+00];[1.392521447307774, -1.1276787455607225, 0.842965894883518, -1.1276787167128701]
+[-1.19712828e-09 -9.84905768e+01  1.59553308e+01];[1.6360554409626689, -1.132349168151169, 0.5578964812110241, -1.1323491337573617]
+[-6.84600843e-10 -9.84542342e+01  2.39461497e+01];[1.850905513108398, -1.130303258443645, 0.2767094148070204, -1.1303032383138958]
+[ -7.97554524 -98.49053237  15.95553497];[1.6387914788420523, -0.8496796348160955, 0.5601111579409364, -1.4001628907618797]
+[ -7.9731158  -98.53373548   7.97311583];[1.3950305622713874, -0.8451543554776602, 0.8451543502236647, -1.3950305747262501]
+[-7.97252873e+00 -9.85436504e+01  9.33690474e-10];[1.1273804612362825, -0.8427317405696713, 1.127380456636922, -1.3921762664478319]
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+[-1.59536155e+01 -9.85093823e+01 -1.77827894e-09];[1.131771440591006, -0.5575576282277958, 1.1317714280645363, -1.6353467371424848]
+[-15.95467293 -98.50002902   7.97507383];[1.3998209005407396, -0.5599369823034954, 0.8494464801402146, -1.6384287057812053]
+[-15.95619839 -98.48973688  15.95619844];[1.6427420438684577, -0.5640166855315409, 0.5640166743737275, -1.6427420466229927]
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+[ -15.91232433 -153.95728621  -23.85947771];[-0.7763410222226681, 0.5945732387268895, 0.2899090439751429, -0.10496984466904641]
+[  -7.95839558 -153.97115268  -23.86188711];[-0.7595516228736359, 0.40145695222019484, 0.30823286233591973, 0.05419356551135641]
+[  -7.95920079 -153.99339983  -15.91546318];[-0.5500373916942883, 0.37336507895150167, 0.15310675217776148, 0.024830496233162413]
+[  -7.95984213 -154.0040708    -7.9598421 ];[-0.3577527195030084, 0.3577527403902573, -0.008412401579149355, 0.008412409049690414]
+[-7.95929813e+00 -1.54007391e+02 -2.34277469e-10];[-0.17810161074356817, 0.3526988854074258, -0.17810161084872034, 0.0031175860976524195]
+[  -7.95984214 -154.00407082    7.95984211];[-0.00841234788921561, 0.35775271744595083, -0.35775266481560847, 0.008412407189724677]
+[  -7.9592008  -153.99339988   15.91546319];[0.15310685037771868, 0.373365018369484, -0.550037288935617, 0.0248304933707746]
+[  -7.95839558 -153.9711527    23.86188711];[0.30823289161089357, 0.40145694343480787, -0.759551572649779, 0.05419355904495248]
+[-4.29744172e-09 -1.53929507e+02  3.17836964e+01];[0.4665676624994627, 0.26863064114934204, -0.9883192860388907, 0.2686306489201995]
+[-4.03595533e-09 -1.53975304e+02  2.38625567e+01];[0.3141601218111123, 0.22234553420255246, -0.7541677726307323, 0.2223455460507567]
+[-2.43205439e-09 -1.53996918e+02  1.59157437e+01];[0.15869016901920704, 0.19394851403387942, -0.5449366479313936, 0.1939485239205503]
+[-1.20653199e-09 -1.54007276e+02  7.95993652e+00];[-0.0031016347828083934, 0.1780998362394592, -0.35271111077113343, 0.17809983551040098]
+[-1.16944316e-09 -1.54010201e+02  3.54693502e-09];[-0.17297150801044714, 0.172971542964274, -0.17297148899371226, 0.17297154548868507]
+[-1.29239901e-09 -1.54007276e+02 -7.95993651e+00];[-0.35271109977277765, 0.1780998210630899, -0.0031015616437427584, 0.1780998267611415]
+[-9.40171269e-10 -1.53996918e+02 -1.59157437e+01];[-0.544936611613013, 0.1939485135254132, 0.15869014899892048, 0.19394851570256427]
+[-6.85218295e-10 -1.53975304e+02 -2.38625567e+01];[-0.7541678245497124, 0.22234555107114046, 0.31416011600949867, 0.2223455515727569]
+[-7.83753108e-10 -1.53929507e+02 -3.17836964e+01];[-0.9883192819005465, 0.2686306376817131, 0.4665676877259051, 0.2686306365641007]
+[   7.95839558 -153.97115275  -23.86188714];[-0.7595514930571716, 0.054193529313145916, 0.30823296133519607, 0.4014569103641507]
+[   7.95920079 -153.99339985  -15.91546319];[-0.5500373575313067, 0.02483048237132349, 0.1531067917338327, 0.37336504490801903]
+[   7.95984214 -154.00407082   -7.95984211];[-0.35775266535694633, 0.008412386520261269, -0.008412356020957508, 0.3577527036488273]
+[ 7.95929813e+00 -1.54007391e+02 -1.32835702e-09];[-0.1781015591019852, 0.003117576099182392, -0.17810156473783936, 0.35269886258095806]
+[   7.95984214 -154.00407085    7.95984211];[-0.008412303560264137, 0.008412383270008709, -0.357752617266784, 0.3577526780804877]
+[   7.95920081 -153.9933999    15.91546319];[0.15310688926729069, 0.024830481264623987, -0.550037242058186, 0.37336498952889274]
+[   7.95839558 -153.97115271   23.86188711];[0.30823291635897937, 0.054193556283340175, -0.7595515543962451, 0.40145692617219864]
+[  15.91232434 -153.95728626   23.85947774];[0.28990910999849967, -0.10496986243442503, -0.7763409181281151, 0.5945731728297635]
+[  15.91445393 -153.98170336   15.91445386];[0.13585304879243712, -0.1358529816342619, -0.5658831034506262, 0.5658831664702517]
+[  15.91410559 -153.99329797    7.95854513];[-0.024837652019741117, -0.15308320341550408, -0.37334356625161325, 0.5499998917479962]
+[ 1.59130371e+01 -1.53996825e+02 -7.46684468e-09];[-0.19393878659462177, -0.15863853133653943, -0.19393882764776632, 0.5448663987932081]
+[  15.91410557 -153.99329794   -7.95854512];[-0.37334365154739446, -0.15308320122706248, -0.02483770382233706, 0.5499999355581611]
+[  15.91445393 -153.98170335  -15.91445385];[-0.5658831380255175, -0.1358529573605925, 0.13585305081222993, 0.5658831720435569]
+[  15.91232436 -153.95728628  -23.85947775];[-0.7763408842094742, -0.10496984634163994, 0.2899091470484478, 0.5945731370475578]
+[  23.85726008 -153.95663038  -15.9109385 ];[-0.594511092036864, -0.2898874292104428, 0.10497037543072246, 0.7762580274189996]
+[  23.85746854 -153.97025569   -7.95706173];[-0.4013839197915976, -0.30817202595168725, -0.054178062267588256, 0.7594046271988193]
+[ 2.38559735e+01 -1.53974156e+02 -7.78427599e-10];[-0.22228759683239324, -0.3140614395464136, -0.2222875983203455, 0.7539568956593095]
+[  23.85746856 -153.97025572    7.95706173];[-0.05417801027239104, -0.3081720031747218, -0.401383860805234, 0.7594045633873946]
+[  23.85726008 -153.95663038   15.9109385 ];[0.10497036861579012, -0.28988742020108293, -0.5945110825942836, 0.7762580193362114]
+[ 3.17730142e+01 -1.53926140e+02  4.45065288e-10];[-0.26846029310859326, -0.4664446427653234, -0.26846028911917696, 0.9878871943932932]
+[ 2.38053583e+01 -1.61814582e+02  4.32129777e-10];[-0.4235079149759233, -0.12237499413511829, -0.4235079091060115, 0.9643341188982757]
+[  15.88997018 -161.84155055  -15.88997006];[-0.768576963284937, 0.05842333743277052, -0.05842319282361214, 0.7685769690600479]
+[  15.89207539 -161.86464416   -7.94852017];[-0.5675602793344782, 0.03770079712600111, -0.21679918301781945, 0.7469728360024164]
+[ 1.58915362e+01 -1.61870000e+02 -3.13322153e-09];[-0.38531055253484076, 0.03117423565943228, -0.385310560941733, 0.7401756381975519]
+[  15.89207542 -161.8646442     7.94852017];[-0.21679912951890068, 0.03770081095809909, -0.5675602085092321, 0.7469727850863739]
+[  15.88997014 -161.84155049   15.88997004];[-0.05842326092355929, 0.05842329967538515, -0.7685770249013709, 0.7685770459245023]
+[   7.94925165 -161.86654554   15.89360665];[-0.03771942795520973, 0.21683845015468828, -0.7470672579197796, 0.5676343286995275]
+[   7.95169962 -161.89084979    7.95169957];[-0.1968493888384284, 0.19684947485096635, -0.5475498632472431, 0.5475499050903474]
+[ 7.95136063e+00 -1.61895472e+02 -7.66991418e-09];[-0.36564803573031807, 0.19051066224293328, -0.36564807341699856, 0.5411559894880109]
+[   7.95169962 -161.89084977   -7.95169957];[-0.5475499148826076, 0.1968494729684792, -0.19684941413913312, 0.547549936677636]
+[   7.94925165 -161.86654557  -15.89360666];[-0.747067240965767, 0.2168384331292609, -0.037719400937252096, 0.5676343202834089]
+[ 9.90582551e-09 -1.61821944e+02 -2.38131906e+01];[-0.9647994467474775, 0.42376954139570644, 0.1223447847967547, 0.42376948819325944]
+[ 2.19378884e-09 -1.61873645e+02 -1.58946050e+01];[-0.7403563344177798, 0.38541582220832443, -0.03120423456531957, 0.3854158067838854]
+[ 2.36962196e-09 -1.61896973e+02 -7.95206420e+00];[-0.5412110751575049, 0.365686820109908, -0.19053308883613718, 0.365686807608816]
+[ 8.48012656e-10 -1.61906652e+02 -1.05321828e-09];[-0.35954449351704293, 0.3595445794456531, -0.3595444966516285, 0.3595445596927977]
+[ 2.43940082e-09 -1.61896973e+02  7.95206421e+00];[-0.1905330338759241, 0.36568682432631494, -0.5412110205198458, 0.36568679742126775]
+[ 6.72438085e-10 -1.61873645e+02  1.58946050e+01];[-0.03120422668003407, 0.3854158213996097, -0.7403563245794563, 0.38541580913945606]
+[ 5.45986914e-10 -1.61821944e+02  2.38131905e+01];[0.12234474810934667, 0.4237695254750018, -0.9647994841972601, 0.42376951678254515]
+[  -7.94925164 -161.86654557   15.89360666];[-0.03771938645324645, 0.5676343487671764, -0.7470672035503226, 0.21683840930364423]
+[  -7.95169961 -161.89084977    7.95169956];[-0.19684936961728314, 0.5475499492777672, -0.5475499030190033, 0.1968494533094095]
+[-7.95136063e+00 -1.61895472e+02 -9.45205195e-09];[-0.36564800775951356, 0.541156002796939, -0.3656480430108721, 0.19051064387359642]
+[  -7.95169961 -161.89084977   -7.95169957];[-0.5475499052839055, 0.5475499501963216, -0.19684939031672638, 0.19684945978677804]
+[  -7.94925165 -161.86654557  -15.89360666];[-0.7470672298720363, 0.567634327973617, -0.037719379624128115, 0.21683842601669095]
+[ -15.88997016 -161.84155056  -15.88997006];[-0.7685769274198722, 0.7685770105652792, -0.05842316359391463, 0.058423280645791505]
+[ -15.89207538 -161.86464415   -7.94852016];[-0.5675602852717877, 0.7469728747079342, -0.216799177254059, 0.037700786638952266]
+[-1.58915362e+01 -1.61870000e+02  8.92694352e-10];[-0.3853105640456399, 0.740175661838179, -0.38531055041884, 0.03117423123518743]
+[ -15.89207541 -161.8646442     7.94852017];[-0.2167991276982233, 0.7469728196302217, -0.5675602087253163, 0.03770079778185541]
+[ -15.88997015 -161.84155051   15.88997004];[-0.058423202581406763, 0.7685770358320038, -0.7685770005801849, 0.05842329974247918]
+[-2.38053583e+01 -1.61814582e+02  2.21034675e-09];[-0.42350794693647736, 0.9643341719245511, -0.42350793614481763, -0.12237500644961113]
+[-1.11507286e-08 -1.69779206e+02  1.88621963e-09];[-0.5701903272034973, 0.570190348714789, -0.5701903114477327, 0.5701903974770745]
diff --git a/lab_AI.md b/lab_AI.md
index 3864153..4e8c90b 100644
--- a/lab_AI.md
+++ b/lab_AI.md
@@ -17,13 +17,20 @@ In this lab you will learn:
 We are going to need third-parties libraries for this lab.
 
 Click the button below to install them:
-#python-button("-m pip install --target 'assets/labs/lab_AI/modules/site-packages' -r 'assets/labs/lab_AI/requirements.txt'")
+#python-button(pyargs=["-m pip install --target", "assets/labs/lab_AI/modules/site-packages", "-r", "assets/labs/lab_AI/requirements.txt"])
+
+This will install the following libraries:
+```
+#include(assets/labs/lab_AI/requirements.txt)
+```
 
 :::
 
 #include(assets/labs/modules/camera_calibration.md)
-#include(assets/labs/lab_AI/sections/3_scikit-learn.md)
+#include(assets/labs/lab_AI/sections/1_dataset.md)
+#include(assets/labs/lab_AI/sections/2_scikit-learn.md)
+#include(assets/labs/lab_AI/sections/3_from_scratch.md)
+#include(assets/labs/lab_AI/sections/4_pytorch.md)
 
 ## Appendix
-#include(assets/labs/lab_AI/sections/change_dataset.md)
-#include(assets/labs/lab_AI/sections/1_dataset.md)
\ No newline at end of file
+#include(assets/labs/lab_AI/sections/change_dataset.md)
\ No newline at end of file
diff --git a/lab_AI_dataset_generation.py b/lab_AI_dataset_generation.py
index 812d8ec..60b0cdb 100644
--- a/lab_AI_dataset_generation.py
+++ b/lab_AI_dataset_generation.py
@@ -35,9 +35,9 @@ def __init__(self, emio, target, effector, assembly, shape, steps=20, direct=Fal
 
         self.animationSteps = steps 
         self.animationStep = self.animationSteps
-        self.motorsAngle = [np.copy(self.emio.getChild(f'Motor{i}').JointActuator.value.value) for i in range(4)]
+        self.motorsAngle = [np.copy(self.emio.getChild(f'Motor{i}').JointActuator.value.value) for i in range(4)] if direct else [np.copy(self.emio.getChild(f'Motor{i}').JointActuator.angle.value) for i in range(4)]
         self.motorsAngleGoals=self.targetsPosition[self.targetIndex]
-        self.motorStep = [(self.motorsAngleGoals[i]-self.emio.getChild(f'Motor{i}').JointActuator.value.value)/(self.animationSteps-1) for i in range(4)]
+        self.motorStep = [(self.motorsAngleGoals[i]-self.emio.getChild(f'Motor{i}').JointActuator.value.value)/(self.animationSteps-1) for i in range(4)] if direct else []
 
         self.direct = direct
 
@@ -61,7 +61,7 @@ def onAnimateBeginEvent(self, _):
                     self.animationStep = self.animationSteps
                     self.effector.effectorGoal = [list(self.targetsPosition[self.targetIndex]) + [0, 0, 0, 1]]
                     self.targetReached = False
-        elif self.assembly.done:
+        elif self.direct and self.assembly.done:
             self.animationStep -= 1
             if self.targetIndex >= 0 and self.animationStep <= 0:
                 self.writeToCSVFile()
diff --git a/sections/1_dataset.md b/sections/1_dataset.md
index 63a31d1..d5e0304 100644
--- a/sections/1_dataset.md
+++ b/sections/1_dataset.md
@@ -1,11 +1,46 @@
-:::::: collapse Dataset Generation
+<a id="datasets"></a>
+:::::: collapse Datasets
+## Datasets 
 
-We can use the simulation to generate the dataset.
-Since we want to train a model that recovers the motors angles based on a desired 3D position of the end effector, the dataset will need these data:
+The datasets used in this lab are in CSV files containing the motors angles and the corresponding end-effector positions of Emio. The datasets are located in the `assets/labs/lab_AI/data/results` folder. Both datasets have the following fields:
 - the four motors angles _m0_, _m1_, _m2_ and _m3_
 - the 3D position of the effector _pos_
 
-The datasets can then be used to train the model. In this lab, we will only use a simple 2-layer perceptron.
+
+### Inverse Simulation
+The inverse simulation is used to get the motors angles based on a desired position for the TCP (Tool Center Part) of the robot (i.e., the center part).
+To create the desired target positions we want, we sample points on geometric shapes; cube or sphere with a ratio defining the distance between two points relative to the shape size.
+
+Two datasets, created in simulation, are available:
+- `blueleg_beam_cube1331.csv`: by sampling 1331 points in a cube
+- `blueleg_beam_sphere515.csv`: by sampling 515 points in a sphere
+
+They have been generated using the SOFA simulation of Emio, with the script `dataset_generation.py`.
+
+You can take a look at `blueleg_beam_cube1331.csv`: 
+#open-button(file="assets/labs/lab_AI/data/results/blueleg_beam_cube1331.csv")
+
+### Direct Simulation
+Here, we explore the workspace by directly applying angle orders to the motors then measuring through the simulation the resulting TCP position.
+
+Two datasets, created in simulation, are available:
+- `blueleg_beam_direct625.csv`: bby combinating **five** possible angles for the four motors, leading nto 625 points
+- `blueleg_beam_direct2401.csv`: by combinating **seven** possible angles for the four motors, leading nto 2401 points
+
+
+You can take a look at `blueleg_beam_direct625.csv`: 
+#open-button(file="assets/labs/lab_AI/data/results/blueleg_beam_direct625.csv")
+
+### Real Robot
+
+Equivalent datasets were recorded on the Emio robot using a high precision magnetic sensor:
+- `blueleg_beam_real_cube2197.csv`: by sampling 2197 points in a cube, contains both the simulated and measured effector positions
+- `blueleg_beam_real_sphere1018.csv`: : by sampling 1018 points in a sphere, contains both the simulated and measured effector positions
+
+These datasets were created by tracking the robot's tool center point (TCP) position with a _Polhemus_ magnetic tracker. These datasets have an extra column `Real Position` with the recorded tracked position.
+
+You can take a look at `blueleg_beam_real_cube2197.csv`: 
+#open-button(file="assets/labs/lab_AI/data/results/blueleg_beam_real_cube2197.csv")
 
 ::::: exercise
 **Generation SOFA Scene:**
@@ -20,14 +55,14 @@ Select the point generation method:
 ::: option direct
 ::::
 
-Ratio of the sampling ]0, 1[ (the higher the coarser): 
+Ratio of the sampling $]0, 1[$ (the higher the coarser) for `sphere` and `cube` options: 
 #input("dataset_ratio", "Ratio to sample (the higher the coarser)", "0.08")
 
-#runsofa-button("assets/labs/lab_AI/lab_AI_dataset_generation.py", "dataset_shape", "dataset_ratio")
+#runsofa-button(file="assets/labs/lab_AI/lab_AI_dataset_generation.py", pyargs=["dataset_shape", "dataset_ratio"])
 
 <br>
 
-Here is is an excerp of the _blueleg_beam_sphere.csv_ dataset file that comes with this lab:
+Here is is an excerp of the _blueleg_beam_sphere515.csv_ dataset file that comes with this lab:
 
 ```text
 # extended ;1
@@ -39,12 +74,11 @@ Here is is an excerp of the _blueleg_beam_sphere.csv_ dataset file that comes wi
 # connector ;bluepart
 # connector type ;rigid
 Effector position;Motor angle
-[-39.96175515 -90.41789743 -39.96175525];[-0.14670205865712832, 0.14670207392254797, 2.43823807942873, -2.438238056118855]
-[-39.95720099 -90.4415037  -31.95609913];[0.1329811350050557, 0.13624487007045172, 2.29165178728331, -2.488099187824528]
-[-39.95397373 -90.45505537 -23.95436099];[0.4217800556565714, 0.13599500085968805, 2.113863614076125, -2.5101582582004642]
-[-39.9514583  -90.46332739 -15.96017202];[0.7233308263521361, 0.14326494422921077, 1.8979428979904553, -2.5098560718291005]
-[-39.95029449 -90.46182801  -7.97640971];[1.0359369002803307, 0.15246389567464924, 1.640800631571854, -2.4992699487352867]
-[-3.99504339e+01 -9.04556845e+01 -8.41130293e-05];[1.3485569542409783, 0.1566254899703859, 1.3478513803217718, -2.4934454150763674]
+[-39.96175319 -90.41790123 -39.9617533 ];[-0.14671493815230305, 0.14671495863621398, 2.438245119379324, -2.438245064719189]
+[-39.95721404 -90.44149463 -31.95606774];[0.13278020542251584, 0.1361462088456899, 2.291810947074616, -2.4880786839009525]
+[-39.95398396 -90.45504974 -23.95431237];[0.42151032888874346, 0.13590327750354508, 2.114101371561853, -2.5101908380204403]
+[-39.9514658  -90.46332978 -15.96010252];[0.7230089875813547, 0.14319760751651572, 1.8982513705899897, -2.5099090246584557]
+[-39.95029801 -90.4618368   -7.97632692];[1.0355881112380285, 0.15242992354764623, 1.6411482937976718, -2.499306559350879]
 ```
 
 :::::
diff --git a/sections/2_scikit-learn.md b/sections/2_scikit-learn.md
new file mode 100644
index 0000000..d4d0214
--- /dev/null
+++ b/sections/2_scikit-learn.md
@@ -0,0 +1,194 @@
+:::: collapse A MLP for robotics with sickit-learn
+
+**Goal**: better understand how neural networks can be used for robotics and simulation
+
+You are going to train an MLP based on different datasets. It is important to understand how those were generated and how they are used in the context of MLP training and evaluation.
+
+First, you will use the _SOFA Robotics simulator_. This is an application specialized in soft robots simulation.
+What you will do in the lab is to try to learn the inverse model of the robot. Meaning that based on where you want the robot _end effector position_ in space, you want the MLP to ouput four _motor angles_ to actuate the robot. Applying these four angles to their respective motors (in _real life_  or in the _simulation_), the end effector should move to the desired position.
+
+In the simulator, you can visualize the error between the desired position and the simulated position of the end effector after applying the angles in the **Plotting Window** as the `error`, `errorX`, `errorY`, `errorZ` which are respectively the Euclidian distance between the two positions (desired and simulated), then projected along the X-, Y-, and Z-axis.
+In real life, when sending the motor angles to the real robot, we can measure the effect of the new motor angles thanks to the camera. This is the error called `camera_to_target_error`.
+Another useful measure is the $r^2$ score of the MLP, it is continuously calculated across all the previous targets.
+
+To train the MLP, you will use datasets. A dataset is then comprised of desired end effector positions and the matching motor angles. The way these datasets are generated can vary and is described in the [previous Datasets section](#datasets).
+
+A summary of this is in the diagram below:
+
+![](assets/labs/lab_AI/data/images/context_diagram.png)
+
+### Train the Model and Test it
+
+In this part, we will use scikit-learn to train a MLP. Scikit-learn is an open-source Python library that provides tools for a wide range of machine learning tasks like including classification, regression, clustering, and dimensionality reduction. Among other functions, it provides the [MLP regressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor) class that we will use to create our first MLP.
+
+We will see in further depth how training works in the next section but to have a first grasp of the **training process**, here is a high-level description: **the goal is to optmize the weigths and bias of the neural network so that it fits our training dataset.** 
+
+To do that, it follows this high-level algorithm:
+
+$$
+\begin{align*}
+    1. & \text{The parameters of the layers (weights and biases) are randomly initialized} \\
+    & \text{For each row of the dataset:} \\
+    & \hskip1em 2. \text{The input (position) from the dataset is passed through the MLP} \\
+    & \hskip1em 3. \text{Get the MLP ouput and the dataset output (the ground truth)} \\
+    & \hskip1em 4. \text{Calculate the gradient on the output data} \\
+    & \hskip1em 5. \text{Backpropagate the gradients} \\
+    & \hskip1em 6. \text{Update the weights and bias of the layers using gradient descent and backpropagation} \\
+    & \hskip1em 7. \text{Loop back to step 2 until maximum number of iterations or epochs is reached}
+\end{align*}
+$$
+
+#### Create a MLP and train it
+Scikit-learn comes with its own implementation of an [MLP regressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor).
+
+You can use it here for a quick exploration of the architecture needed.
+
+Several (hyper-)parameters can be played with. Here are some of the parameters:
+- the sizes of the layers
+- the activation function for all neurons (`identity`, `logistic`, `tanh`, `relu`)
+- the solver/optimizer for the gradient descent (`lbfgs`, `sgd`, `adam`)
+- the batch size, for `sgd`, `adam` solvers, until update of the weights and biais
+- the maximum count of iterations (epochs for `sgd`, `adam` solvers)
+
+```python
+from sklearn.neural_network import MLPRegressor
+
+# creates a MLP with one hidden layer of 100 neurons, the 'adam' optimizer and will train on a maximum of 500 epochs
+mlp = MLPRegressor(hidden_layer_sizes=(100,), solver='adam',
+                   max_iter=500,) 
+
+mlp.fit(X_train, y_train) # train the model using the X_train dataframe of the features and y_train as the target dataframe
+```
+
+In the code above, since our features are the components of a 3D position, we have 3 features as input. Regarding the output, since we want the 4 angles of the 4 motors, the output of the MLP is four values.
+
+::: exercise
+**Exercise 1**
+
+1. Create an MLP with two hidden layers of _128_ nodes each and that will train on _20000_ epochs in the `modules/sklearn_MLP.py`
+    #open-button(file="assets/labs/lab_AI/modules/sklearn_MLP.py")
+
+2. Train it: 
+    #python-button(file="assets/labs/lab_AI/train_model.py", pyargs=["scikit-learn", "assets/labs/lab_AI/data/results/blueleg_beam_sphere515.csv"])
+
+    The trained model save path is `data/results/model_sklearn.joblib`
+
+Note that we used the dataset called `blueleg_beam_sphere515.csv`. This is because we generated it using **an inverse model** (to be presented next time) of Emio configured with the **blue legs**, the **beam** model, and data points sampled on a **sphere**. 
+
+:::
+
+#### Evaluate the model
+
+In Machine learning, an common evaluation metric is the $r^{2}$ score or coefficient of determination. Essentially, it measures the proportion of the variance in the dependent variable that is predictable from the independent variables in the model.
+
+A high value indicates that the models highly fits the data.
+
+The general mathematical definition is:
+
+$$
+\def\ssres{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}
+\def\sstot{\sum_{i=1}^{n} (y_i - \bar{y})^2}
+
+r^2 = 1 - \frac{\ssres}{\sstot}
+
+\\[1em]
+
+\begin{array}{ll}
+\text{where:} \\
+\quad y_i & \text{predicted value} \\
+\quad \hat{y}_i & \text{observed data point} \\
+\quad \bar{y} & \text{mean of observed data points}
+\end{array}
+$$
+
+
+##### Without the simulation
+You can use the [MLPRegressor.score](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor.score) method to calculate the coefficient of determination on the test data.
+
+```python
+mlp.score(X_test, y_test)
+```
+
+::: exercise
+**Exercise 2**
+
+Let's see the performance of our model. Calculate the score of the model by pressing the button below:
+
+#python-button(file="assets/labs/lab_AI/evaluate_model.py", pyargs=["scikit-learn", "assets/labs/lab_AI/data/results/blueleg_beam_cube1331.csv", "assets/labs/lab_AI/data/results/model_sklearn.joblib"])
+
+*Note*: we are testing the model on another dataset: *blueleg_beam_cube1331.csv*
+
+You should have a score that is quite low. This is mostly due to the fact the MLP is using relu as an activation function. However, if you look at the dataset, you have lots of negative values because of the where the reference frame of Emio is.
+
+1. To avoid this problem, use the `logistic` activation function in `modules/sklearn_MLP.py`, train and calculate the score again:
+#open-button(file="assets/labs/lab_AI/modules/sklearn_MLP.py")
+
+2. Train again
+    #python-button(file="assets/labs/lab_AI/train_model.py", pyargs=["scikit-learn", "assets/labs/lab_AI/data/results/blueleg_beam_cube1331.csv"])
+
+3. Calculate the $r^2$ score again
+    #python-button(file="assets/labs/lab_AI/evaluate_model.py", pyargs=["scikit-learn", "assets/labs/lab_AI/data/results/blueleg_beam_cube1331.csv", "assets/labs/lab_AI/data/results/model_sklearn.joblib"])
+
+:::
+
+
+##### With the SOFA simulation
+Now that you have a theoretically good-enough model, lets use it in simulation!
+
+The trained model will be used to compute the robot’s inverse kinematics; that is, for a desired position in space, the MLP will provide the corresponding motor positions. This is the foundation of control and motion planning in robotics.
+
+In the **Plotting** window, you can see the $r^2$ score calculated over the last points.
+
+::: exercise
+**Exercise 3**
+
+Use your model in the SOFA scene.
+
+---
+
+***First test: Manual Position Control of the Robot***
+
+Using the sliders in the _My Robot_ window, you can control the x, y, and z desired/target effector position.  
+This allows you to manually test different robot configurations, and for each one, measure the error between:  
+- the desired position,  
+- the simulated model position (which we'll discuss next time), and  
+- the position measured by the camera.
+
+| ![](assets/labs/lab_AI/data/images/Pos3_EmioTest.png){width=90%} | ![](assets/labs/lab_AI/data/images/Pos1_EmioTest.png){width=90%} | ![](assets/labs/lab_AI/data/images/Pos2_EmioTest.png){width=90%} |
+|:--:|:--:|:--:|
+
+
+#runsofa-button(file="assets/labs/lab_AI/lab_AI_test.py", pyargs=["scikit-learn", "data/results/model_sklearn.joblib", "notargets", "0.4"])
+
+
+**Questions**  
+1. Is the error between the desired position and the simulated position always the same depending on the the desired position?  
+2. How does the error vary with respect to the position measured from the camera (`camera_to_target_error`)?
+3. At this stage, can you provide a first analysis of the errors?
+
+---
+
+***Second Test: More systematic*** 
+Here, we propose to perform a systematic scan of positions in the form of a grid of points evenly spaced on a plane.The white dots are the targets (desired positions) and the red dots are the positions of the end effector after applying the angles output from the MLP.
+
+#runsofa-button(file="assets/labs/lab_AI/lab_AI_test.py", pyargs=["scikit-learn", "data/results/model_sklearn.joblib", "plane", "ratio_sklearn"])
+
+By default, the spacing is set to `0.1`, meaning the spacing is given by the plane size divided by 10.
+To change this spacing, you can enter a number in $]0, 1[$: 
+#input("ratio_sklearn", "Ratio for sampling", "0.1")
+
+**Questions**  
+1. After letting the simulation run through all the targets, what conclusions can be drawn from this?
+2. Now it’s your turn ! What strategy can you apply to improve the learning ? (not mandatory, but see some possibilities in the next sections)
+
+---
+
+**Additional note:**
+This is similar to the previous simulation, but here you can visualize the entire set of points used for training.
+
+#runsofa-button(file="assets/labs/lab_AI/lab_AI_test.py", pyargs=["scikit-learn", "data/results/model_sklearn.joblib", "plane", "ratio_sklearn", "data/results/blueleg_beam_sphere515.csv"])
+
+| ![](assets/labs/lab_AI/data/images/Workspace.png){width=90%} 
+|:--:|
+
+::::
\ No newline at end of file
diff --git a/sections/2_from_scratch.md b/sections/3_from_scratch.md
similarity index 92%
rename from sections/2_from_scratch.md
rename to sections/3_from_scratch.md
index e67bfb4..59417e2 100644
--- a/sections/2_from_scratch.md
+++ b/sections/3_from_scratch.md
@@ -41,6 +41,8 @@ It is possible to directly use the solutions provided by Scikit-learn: `mean_squ
 ## Initialization of the parameters and Forward Propagation
 We are going to build an MLP with two hidden layers of 128 neurons each. What are the dimensions of the input and output?
 
+::: exercise 
+**Exercise 1**
 Complete the following code:  
  ```python
     def initialization(input_dim, n1, n2, output_dim):
@@ -67,7 +69,9 @@ Click here to see the solution :
 
 #open-button("initialization.py")
 
-The foarward propagation refers to the computation of the output of the network based on the input.
+:::
+
+The forward propagation refers to the computation of the output of the network based on the input.
 
 Let  _A_ our activation function and $\mathcal{L}$  our loss function. 
 We use the __logistic__ function as the activation and the __mean squared error__ as loss in this lab.
@@ -79,6 +83,9 @@ $L=\frac{1}{m}\sum_{i=1}^{m}(y_i-\hat{y}_i)^2$
 Where _i_ is the index of the layer, _X_ the input, _W_ the weights, _b_ the bias, $\hat{y}$ the true output and `m` the number of samples.
 
 
+::: exercise 
+**Exercise 2**
+
 Complete the following code:
 ```python
     def forward_propagation(X, parametres):
@@ -111,13 +118,18 @@ Complete the following code:
 Click here to see the solution : 
 #open-button("forward_propagation.py")
 
+:::
+
 
 ## Backpropagation
 Backpropagation is an algorithm used to train neural networks by adjusting weights. It calculates the error between the predicted output and the actual output (loss) and propagates it backward through the network's layers. This is because the input of layer _i_ is the output of layer _i-1_.
 
 The partial derivatives (gradient) of the loss with respect to the weights W and biais b are used to update them via gradient descent.
 
-Use the forward propagation to find the back propagation by expressing the following expressions that will be used for the gradient descent since W and b are what we want to optimize during the training. Use the chain rule as in the first expression:
+::: exercise 
+**Exercise 3**
+
+1. Use the forward propagation to find the back propagation by expressing the following expressions that will be used for the gradient descent since W and b are what we want to optimize during the training. Use the chain rule as in the first expression:
 
 $\frac{\partial \mathcal{L}}{\partial W_3} = \frac{\partial \mathcal{L}}{\partial A_3}*\frac{\partial A_3}{\partial Z_3}*\frac{\partial Z_3}{\partial W_3} = ...$
 
@@ -131,8 +143,7 @@ $\frac{\partial \mathcal{L}}{\partial W_1} = ...$
 
 $\frac{\partial \mathcal{L}}{\partial b_1} = ...$
 
-
-When it is done complete the following code:
+2. When it is done complete the following code:
 
 ```python
     def back_propagation(X, y, parametres, activations):
@@ -171,6 +182,8 @@ When it is done complete the following code:
 Click here to see the solution in code : 
 #open-button("back_propagation.py")
 
+:::
+
 For the gradient descent, we use the easiest implementation. If you want, you can try to improve it, try using Adam instead. 
 ```python
    def update(gradients, parametres, learning_rate):
@@ -216,7 +229,7 @@ You can find the rest of the code by clicking here :
 ## Train it
 
 ::: exercise 
-**Exercise**
+**Exercise 4**
 
 1. Complete the `train`function of `custom_MLP.py`
 
@@ -236,7 +249,7 @@ python train_model.py <model_type> <dataset_path>
 ## Evaluate it
 ### Without SOFA
 ::: exercise
-**Exercise**
+**Exercise 5**
 1. Implement the `score` in `modules/pytorch_MLP.py`.
 Use the r2_score_pytorch` function.
 
@@ -258,7 +271,7 @@ python evaluate_model.py <model_type> <dataset_path> <model_path>
 
 ### With SOFA
 ::: exercise
-**Exercise 8**
+**Exercise 6**
 
 Use your model in the SOFA scene
 
diff --git a/sections/3_scikit-learn.md b/sections/3_scikit-learn.md
deleted file mode 100644
index 7c8a896..0000000
--- a/sections/3_scikit-learn.md
+++ /dev/null
@@ -1,175 +0,0 @@
-:::: collapse A MLP with sickit-learn
-
-You are going to train an MLP based on different datasets. It is important to understand how those were generated and what is going on.
-
-First, you will use the SOFA Robotics simulator. This is an application specialized in soft robots simulation.
-What you will do in the lab is to try to learn the inverse model of the robot. Meaning that based on where you want the robot end effector in space, you want the MLP to ouput four motor angles to actuate the robot. Applying these four angles to their respective motors (in real life or in the simulation), the end effector should move to the desired position.
-
-In the simulator, you can visualize the error between the desired position and the simulated position of the end effector after applying the angles in the **Plotting Window** as the `error`, `errorX`, `errorY`, `errorZ` which are respectively the Euclidian distance between the two positions (desired and simulated), then projected along the X-, Y-, and Z-axis.
-In real life, when sending the motor angles to the real robot, we can measure the effect of the new motor angles thanks to the camera. This is the error called `camera_to_target_error`.
-Another useful measure is the r2 score of the MLP, it is continuously calculated across all the previous targets.
-
-To train the MLP, you will use datasets. A dataset is then comprised of desired end effector positions and the matching motor angles. The way these datasets are generated can vary. For more details, you can take a look at the appendix below.
-A summary of this is in the diagram below:
-![](assets/labs/lab_AI/data/images/context_diagram.png)
-
-### Train the Model and Test it
-
-#### Create a MLP and train it
-Scikit-learn comes with its own implementation of an [MLP regressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor).
-
-You can use it here for a quick exploration of the architexture needed.
-
-Several (hyper-)parameters be played with. Here are some of the parameters:
-- the number of layers and their sizes
-- the activation function for all neurons
-- the solver/optimizer for the gradient descent
-- the batch size
-- the maximum count of iterations
-
-
-```python
-from sklearn.neural_network import MLPRegressor
-
-# creates a MLP with one hidden layer of 100 neurons, the 'adam' optimizer and will train on a maximum of 500 epochs
-mlp = MLPRegressor(hidden_layer_sizes=(100,), solver='adam',
-                   max_iter=500,) 
-
-mlp.fit(X_train, y_train) # train the model using the X_train dataframe of the features and y_train as the target dataframe
-```
-
-In the code above, since our features the components of a 3D point, we have 3 features as input. Regarding the output, since we want the 4 angles of the 4 motors, the output of the MLP is four values.
-
-::: exercise
-**Exercise 1**
-
-1. Create an MLP with two hidden layers of _128_ nodes each and that will train on _20000_ epochs in the `modules/sklearn_MLP.py`
-    #open-button("assets/labs/lab_AI/modules/sklearn_MLP.py")
-
-2. Train it: 
-    #python-button("'assets/labs/lab_AI/train_model.py' scikit-learn 'assets/labs/lab_AI/data/results/blueleg_beam_sphere.csv'")
-
-    If the above button does not work run the following:
-    ```bash
-    cd ~/emio-labs/v25.12.01/labs/lab_AI # Linux
-    cd "%USERPROFILE%\emio-labs\v25.12.01\assets\labs\lab_AI" # Windows
-    python train_model.py scikit-learn data/results/blueleg_beam_sphere.csv
-    ```
-    
-    The trained model save path is `data/results/model_sklearn.joblib`
-
-Note that we used the dataset called `blueleg_beam_sphere.csv`. This is because we generated it using **an inverse model** (to be presented next time) of Emio configured with the **blue legs**, the **beam** model, and data points sampled on a **sphere**. 
-
-:::
-
-#### Evaluate the model
-
-##### Without the simulation
-You can use the [MLPRegressor.score](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor.score) method to calculate the coefficient of determination on the test data.
-
-```python
-mlp.score(X_test, y_test)
-```
-
-::: exercise
-**Exercise 2**
-
-Let's see the perpformance of our model. Calculate the score of the model by pressing the button below:
-
-#python-button("'assets/labs/lab_AI/evaluate_model.py' scikit-learn 'assets/labs/lab_AI/data/results/blueleg_beam_cube.csv' 'assets/labs/lab_AI/data/results/model_sklearn.joblib'")
-
-If the above button does not work run the following:
-```bash
-cd ~/emio-labs/v25.12.01/labs/lab_AI # Linux
-cd "%USERPROFILE%\emio-labs\v25.12.01\assets\labs\lab_AI" # Windows
-python evaluate_model.py scikit-learn data/results/blueleg_beam_cube.csv assets/labs/lab_AI/data/results/model_sklearn.joblib
-```
-
-*Note*: we are testing the model on another dataset: *blueleg_beam_cube.csv*
-
-You should have a score that is quite low. This is mostly due to the fact the MLP is using relu as an activation function. However, if you look at the dataset, you have lots of negative values because of the where the reference frame of Emio is.
-
-1. To avoid this problem, use the `logistic` activation function in `modules/sklearn_MLP.py`, train and calculate the score again:
-#open-button("assets/labs/lab_AI/modules/sklearn_MLP.py")
-
-2. Train again
-    #python-button("'assets/labs/lab_AI/train_model.py' scikit-learn 'assets/labs/lab_AI/data/results/blueleg_beam_sphere.csv'")
-
-    If the above button does not work run the following:
-    ```bash
-    cd ~/emio-labs/v25.12.01/labs/lab_AI # Linux
-    cd "%USERPROFILE%\emio-labs\v25.12.01\assets\labs\lab_AI" # Windows
-    python train_model.py scikit-learn data/results/blueleg_beam_sphere.csv
-
-    ```
-3. Calculate the r2 score again
-    #python-button("'assets/labs/lab_AI/evaluate_model.py' scikit-learn 'assets/labs/lab_AI/data/results/blueleg_beam_cube.csv' 'assets/labs/lab_AI/data/results/model_sklearn.joblib'")
-
-    If the above button does not work run the following:
-    ```bash
-    cd ~/emio-labs/v25.12.01/labs/lab_AI # Linux
-    cd "%USERPROFILE%\emio-labs\v25.12.01\assets\labs\lab_AI" # Windows
-    python evaluate_model.py scikit-learn data/results/blueleg_beam_cube.csv assets/labs/lab_AI/data/results/model_sklearn.joblib
-    ```
-
-:::
-
-
-##### With the SOFA simulation
-Now that you have a theoretically good-enough model, lets use it in simulation!
-
-The trained model will be used to compute the robot’s inverse kinematics; that is, for a desired position in space, the MLP will provide the corresponding motor positions. This is the foundation of control and motion planning in robotics.
-
-::: exercise
-**Exercise 3**
-
-Use your model in the SOFA scene.
-
----
-
-***First test: Manual Position Control of the Robot***
-
-Using the sliders in the _My Robot_ window, you can control the x, y, and z desired/target effector position.  
-This allows you to manually test different robot configurations, and for each one, measure the error between:  
-- the desired position,  
-- the simulated model position (which we'll discuss next time), and  
-- the position measured by the camera.
-
-| ![](assets/labs/lab_AI/data/images/Pos3_EmioTest.png){width=90%} | ![](assets/labs/lab_AI/data/images/Pos1_EmioTest.png){width=90%} | ![](assets/labs/lab_AI/data/images/Pos2_EmioTest.png){width=90%} |
-|:--:|:--:|:--:|
-
-
-#runsofa-button("assets/labs/lab_AI/lab_AI_test.py", "scikit-learn", "data/results/model_sklearn.joblib", "notargets", "0.4")
-
---- 
-> **Questions**  
-> Is the error between the desired position and the simulated position always the same depending on the the desired position?  
-> How does the error vary with respect to the position measured from the camera (camera_to_target_error)?
-> At this stage, can you provide a first analysis of the errors?
----
-
-***Second Test: More systematic*** 
-Here, we propose to perform a systematic scan of positions in the form of a grid of points evenly spaced on a plane.The white dots are the targets (desired positions) and the red dots are the positions of the end effector after applying the angles output from the MLP.
-
-#runsofa-button("assets/labs/lab_AI/lab_AI_test.py", "scikit-learn", "data/results/model_sklearn.joblib", "plane", "ratio_sklearn")
-
-By default, the spacing is set to `0.1`, meaning the spacing is given by the plane size divided by 10.
-To change this spacing, you can enter a number in ]0, 1[: 
-#input("ratio_sklearn", "Ratio for sampling", "0.1")
---- 
-> **Questions**  
-> After letting the simulation run through all the targets, what conclusions can be drawn from this?
-> Now it’s your turn ! What strategy can you apply to improve the learning ? (not mandatory, but see some possibilities in the next sections)
-
----
-
->**Additional note:**
-> This is similar to the previous simulation, but here you can visualize the entire set of points used for training.
-
-#runsofa-button("assets/labs/lab_AI/lab_AI_test.py", "scikit-learn", "data/results/model_sklearn.joblib", "plane", "ratio_sklearn", "data/results/blueleg_beam_sphere.csv")
-
-| ![](assets/labs/lab_AI/data/images/Workspace.png){width=90%} 
-|:--:|
-
-*** ***
-::::
\ No newline at end of file
diff --git a/sections/4_pytorch.md b/sections/4_pytorch.md
index 73cc884..cd2bf71 100644
--- a/sections/4_pytorch.md
+++ b/sections/4_pytorch.md
@@ -1,9 +1,32 @@
 :::: collapse An MLP with PyTorch
-### PyTorch Installation
+
+PyTorch is an open-source machine learning framework primarily designed for deep learning, offering a dynamic computational graph that makes model development highly flexible and intuitive. It is widely favored by researchers and practitioners for its Pythonic interface and ease of use when tackling complex neural network architectures.
+
+PyTorch provides an easy way to create neural networks by subclassing the `torch.nn.Module` class, and implementing the `__init__` and `forward`  methods.
+
+```python
+class myNeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+        self.fc1 = nn.Linear(10, 4)
+        self.fc2 = nn.Linear(4, 2)
+
+    def forward(self, x)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return x
+```
+
+The above code implements the following neural network that takes an input of 10 values, that is fed to a hidden layer taking 4 input values, and an output layer with two values.
+Both of the activation functions are ReLu funcionts.
+
+![](assets/labs/lab_AI/data/images/nn.png){width="50%"}{class="center"}
+
 
 ### Your First MLP with PyTorch
 
-Create a MLP with two hidden layers of 128 nodes each and that will train on 20000 epochs. Given that you already have the x_train, y_train, x_test and y_test numpy rrays from the previous section.
+Create a MLP with two hidden layers of 128 nodes each and that will train on 20000 epochs. Given that you already have the x_train, y_train, x_test and y_test numpy arrays from the previous section.
 
 ```python
 import torch
@@ -16,7 +39,7 @@ from sklearn.preprocessing import StandardScaler
 
 
 ::: exercise
-**Exercise 4**
+**Exercise 1**
 Create a PyTorch version of the MLP with 2 layers of 128 neurons, and a logistic activation function.
 
 For the layers, use the `nn.Linear`. The logisitic function is `nn.Sigmoid()`
@@ -27,7 +50,7 @@ Now that you have your network, we need to tell it how to process data from the
 
 
 ::: exercise
-**Exercise 5**
+**Exercise 2**
 
 Implement the `forward` method that implements the fact that the X input are to be processed by the first layer, then the result by the second layer then outputs the result. 
 
@@ -78,7 +101,7 @@ You probably wonder how to compute the loss and what is the optimizer.
 
 
 ::: exercise
-**Exercise 6**
+**Exercise 3**
 
 1. Code the training loop to train your model.
 It should train on 20000 epochs.
@@ -101,7 +124,7 @@ python train_model.py <model_type> <dataset_path>
 ### Evaluate it
 
 ::: exercise
-**Exercise 7**
+**Exercise 4**
 
 1. Implement the `score` in `modules/pytorch_MLP.py`.
 Use the r2_score_pytorch` function.
@@ -122,7 +145,7 @@ python evaluate_model.py <model_type> <dataset_path> <model_path>
 :::
 
 ::: exercise
-**Exercise 8**
+**Exercise 5**
 
 Use your model in the SOFA scene