infra: add PrimitiveFrameCfg; isaaclab: support it

[Fisher-Wang]

This PR adds PrimitiveFrameCfg object cfg to infra to visualize the pose of specific object or body link better. Currently, isaaclab support it.

The functionality can be tested using the following script:

from __future__ import annotations

import torch

from metasim.cfg.objects import PrimitiveFrameCfg
from metasim.cfg.scenario import ScenarioCfg
from metasim.constants import SimType
from metasim.utils.setup_util import get_sim_env_class

scenario = ScenarioCfg(
    robot="franka",
    try_add_table=False,
    sim="isaaclab",
    num_envs=1,
)

scenario.objects = [
    PrimitiveFrameCfg(
        name="frame",
        base_link=("franka", "panda_hand"),
        # ! feel free to change this to the following
        # base_link="franka",
        # base_link=None,
        scale=0.1,
    ),
]
env_class = get_sim_env_class(SimType(scenario.sim))
env = env_class(scenario)

init_states = [
    {
        "objects": {},
        "robots": {
            "franka": {
                "pos": torch.tensor([0.0, 0.0, 0.0]),
                "rot": torch.tensor([1.0, 0.0, 0.0, 0.0]),
                "dof_pos": {
                    "panda_joint1": 0.0,
                    "panda_joint2": -0.785398,
                    "panda_joint3": 0.0,
                    "panda_joint4": -2.356194,
                    "panda_joint5": 0.0,
                    "panda_joint6": 1.570796,
                    "panda_joint7": 0.785398,
                    "panda_finger_joint1": 0.04,
                    "panda_finger_joint2": 0.04,
                },
            },
        },
    }
]
obs, extras = env.reset(states=init_states)
robot_joint_limits = scenario.robot.joint_limits
while True:
    actions = [
        {
            "dof_pos_target": {
                joint_name: (
                    torch.rand(1).item() * (robot_joint_limits[joint_name][1] - robot_joint_limits[joint_name][0])
                    + robot_joint_limits[joint_name][0]
                )
                for joint_name in robot_joint_limits.keys()
            }
        }
        for _ in range(scenario.num_envs)
    ]
    obs, reward, success, time_out, extras = env.step(actions)

Expected behavior:

iShot_2025-04-21_21.32.04.mp4

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