"Could you please demonstrate the information of each dimension corresponding to the state dictionary inside the obs in the dataset?" about maniskill HOT 7 CLOSED

Please see the get_obs function in the parent env (https://github.com/haosulab/ManiSkill2/blob/fc08823bf96791946591a508f336d724fa7cad26/mani_skill2/envs/sapien_env.py#L255) and in the child env for more details. The state contains 2 parts: get_obs_agent() (agent proprioceptive states) and get_obs_extra() (other environment-specific states). Different environments have different state dimensions. The state observation mode has more state dimensions than the visual (rgbd/pointcloud) observation modes, since the former contains ground truth object pose information in get_obs_extra(), while the latter does not.

If you are not using a wrapper (e.g., ManiSkill2-Learn), then if you are under visual observation mode (rgbd & pointcloud) and print out env.get_obs(), the dictionary keys will tell the meanings.

If you are using the state observation mode, then in particular, for PickCube-v0, the state dimension is 51, which contains (1) from get_obs_agent() (see https://github.com/haosulab/ManiSkill2/blob/fc08823bf96791946591a508f336d724fa7cad26/mani_skill2/agents/base_agent.py#L170), agent qpos (state[:9]) & qvel (state[9:18]) & agent base pose (state[18:25]) & controller state (empty) (these dimensions are robot-specific); (2) from get_obs_extra(), agent tcp pose (state[25:32]), goal pos (state[32:35]), tcp to goal pos (state[35:38]), cube pose (state[38:45]), tcp to obj pos (state[45:48]), obj to goal pos (state[48:51])

If you are using ManiSkill2-Learn wrapper and using any of the visual observation modes (which I guess it's your case), see https://github.com/haosulab/ManiSkill2-Learn/blob/83dfe26c73b6ce6b0388a0fa07493f340e36dd44/maniskill2_learn/env/wrappers.py#L235 for the environment wrapper. For the 38-dimensional "state" output from the wrapper, it contains agent qpos (state[:9]) & qvel (state[9:18]) & agent base_pose (state[18:25]) & agent tcp_pose (state[25:32]) & goal pos (state[32:35]) & tcp_to_goal_pos (state[35:38])

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Why does "agent qpos" have 9 dimensions, and what does each dimension represent?

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The first 7 dimensions are panda joint positions; the last 2 dimensions are panda gripper positions

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what is the mean of the base_pose and tcp_bose

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Pose = concat(xyz position, rotation), rotation is represented in the format of quaternion in our state space
base_pose = pose of robot base
tcp_pose = pose of robot tool center point (mid point between two gripper fingers)

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May I know the respective value ranges for these 38 dimensions？

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States corresponding to robot qpos are bounded by joint angle ranges. Other dims are unbounded.

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