From d6230a887f3060d795334c2733864de4758191fb Mon Sep 17 00:00:00 2001 From: UWLab BOT Date: Sun, 4 Jan 2026 20:46:00 -0800 Subject: [PATCH] Prepares pre-merge --- docs/source/publications/omnireset/index.rst | 108 +++++++++++++++++-- 1 file changed, 102 insertions(+), 6 deletions(-) diff --git a/docs/source/publications/omnireset/index.rst b/docs/source/publications/omnireset/index.rst index 73071bd..5b2e1e2 100644 --- a/docs/source/publications/omnireset/index.rst +++ b/docs/source/publications/omnireset/index.rst @@ -182,6 +182,10 @@ Reproduce our training results from scratch. .. tab-item:: Leg Twisting + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -198,7 +202,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -214,8 +218,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=fbleg env.scene.receptive_object=fbtabletop + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=fbleg env.scene.receptive_object=fbtabletop + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \ @@ -232,6 +248,10 @@ Reproduce our training results from scratch. .. tab-item:: Drawer Assembly + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -248,7 +268,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -264,8 +284,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=fbdrawerbottom env.scene.receptive_object=fbdrawerbox + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=fbdrawerbottom env.scene.receptive_object=fbdrawerbox + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \ @@ -282,6 +314,10 @@ Reproduce our training results from scratch. .. tab-item:: Peg Insertion + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -298,7 +334,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -314,8 +350,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=peg env.scene.receptive_object=peghole + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=peg env.scene.receptive_object=peghole + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \ @@ -332,6 +380,10 @@ Reproduce our training results from scratch. .. tab-item:: Rectangle on Wall + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -348,7 +400,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -364,8 +416,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=rectangle env.scene.receptive_object=wall + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=rectangle env.scene.receptive_object=wall + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \ @@ -382,6 +446,10 @@ Reproduce our training results from scratch. .. tab-item:: Cube Stacking + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -398,7 +466,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -414,8 +482,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=cube env.scene.receptive_object=cube + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=cube env.scene.receptive_object=cube + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \ @@ -432,6 +512,10 @@ Reproduce our training results from scratch. .. tab-item:: Cupcake on Plate + .. note:: + + **Skip directly to Step 4** if you want to train an RL policy with our pre-generated reset state datasets. Only run Steps 1-3 if you want to generate your own. + **Step 1: Collect Partial Assemblies** (~30 seconds) .. code:: bash @@ -448,7 +532,7 @@ Reproduce our training results from scratch. .. important:: - Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` are set appropriately. + Before running, make sure ``base_path`` and ``base_paths`` in ``reset_states_cfg.py`` point to your dataset directories. .. code:: bash @@ -464,8 +548,20 @@ Reproduce our training results from scratch. # Object Partially Assembled, End-Effector Grasped (Near Goal) python scripts_v2/tools/record_reset_states.py --task OmniReset-UR5eRobotiq2f85-ObjectPartiallyAssembledEEGrasped-v0 --num_envs 4096 --num_reset_states 10000 --headless --dataset_dir ./reset_state_datasets/ObjectPartiallyAssembledEEGrasped env.scene.insertive_object=cupcake env.scene.receptive_object=plate + **Step 3.5: Visualize Reset States (Optional)** + + Visualize the generated reset states to verify they are correct. + + .. code:: bash + + python scripts_v2/tools/visualize_reset_states.py --task OmniReset-Ur5eRobotiq2f85-RelCartesianOSC-State-Play-v0 --num_envs 4 --dataset_dir ./reset_state_datasets env.scene.insertive_object=cupcake env.scene.receptive_object=plate + **Step 4: Train RL Policy** + .. important:: + + If you generated your own datasets in Steps 1-3, make sure to update ``base_paths`` in ``rl_state_cfg.py`` to point to your dataset directories. + .. code:: bash python -m torch.distributed.run \