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license: cc-by-nc-4.0
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pretty_name: "RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios"
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task_categories:
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---
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# RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios
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This repo contains several demonstration trajectories generated by [RoboCAS-v0](https://github.com/notFoundThisPerson/RoboCAS-v0).
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Details can be found on GitHub.
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## Cite
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```
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@inproceedings{zheng2024robocas
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license: cc-by-nc-4.0
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pretty_name: "RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios"
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task_categories:
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- robotics
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---
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# RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios
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This repo contains several demonstration trajectories generated by [RoboCAS-v0](https://github.com/notFoundThisPerson/RoboCAS-v0).
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Details can be found on GitHub.
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## Dataset Structure
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Trajectories are contained in the zip files,each contains the following tasks:
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```
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scattered.zip: Picking task in the scattered scene.
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orderly.zip: Selecting task in the orderly scene.
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stacked.zip: Searching task in the stacked scene.
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```
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Details can be found in the paper.
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Each trajectory is arranged as the following structure:
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```
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.
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|-- data_info.json # Overview of the dataset information, in dictionary format.
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|-- episode_0000000 # Trajectory 0.
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| |-- episode_info.npz # Actions and robot states in the trajectory.
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| |-- gripper_camera # Data of the camera mounted on the gripper.
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| | |-- cam_pos_wrt_${parent}.npy # Camera pose w.r.t. its parent link.
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| | |-- intrinsic.npy # Intrinsic of this camera.
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| | |-- rgb # RGB images in this trajectory.
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| | | |-- 0000.png # Image at step 0.
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| | | |-- ...
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| | |-- depth # Depth images in this trajectory. Same structure as "rgb".
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| | |-- ...
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| |-- base_camera # Data of the camera mounted on the robot base. Same structure as "gripper_camera".
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| | |-- ...
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| |-- static_camera # Data of the camera mounted on the ground. Same structure as "gripper_camera".
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| | |-- ...
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|-- episode_0000001 # Same structure as "episode_0000000".
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|-- ...
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```
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In each trajectory folder, the *episode_info.npz* file contains the trajectory of the agent, the structure and the explanations of each item is as follows.
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All of the Quaternion in our dataset are stored in "xyzw" order.
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All relative transforms are calculated under the frame of the last time step, i.e. $\Delta T_{t} = T_{t-1}^{-1} T_{t}$, where $\Delta T_{t}, T_{t} \in SE(3)$ are the relative action and the pose at time step $t$.
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```
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rel_pos # Relative position shift of the EEF w.r.t. the last EEF pose in Cartesian coordinate.
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rel_orn # Relative orientation shift of the EEF w.r.t. the last EEF pose in Quaternion.
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ee_pos # Absolute position of the EEF w.r.t. the arm base in Cartesian coordinate.
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ee_orn # Absolute orientation of the EEF w.r.t. the arm base in Quaternion.
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robot_joints # Joint angles of the arm.
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arm_joint_vel # Velocities of the arm joints.
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base_pos # Position of the arm base w.r.t. the world in Cartesian coordinate.
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base_orn # Orientation of the arm base w.r.t. the world in Quaternion.
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base_rel_pos # Relative position shift of the arm base w.r.t. the the pose in last step in Cartesian coordinate.
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base_rel_orn # Relative orientation shift of the arm base w.r.t. the the pose in last step in Quaternion.
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gripper_width # Open width of the gripper fingers.
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gripper_status # Open/close command of the gripper.
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episode_length # Length of the trajectory.
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language_goal # Global goal instruction of this trajectory.
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language_embedding # Embedding of the goal instruction generated by \href{https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2}{Mini LAMMA}.
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step_lang_goals # Goal annotation of the sub-task for each step of action in this trajectory.
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step_goal_embs # Embedding of step goals generated by \href{https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2}{Mini LAMMA}.
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step_goal_type # Type of the sub-task goals in each step.
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```
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## Cite
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```
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@inproceedings{zheng2024robocas
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