File size: 1,878 Bytes
35f73a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | # Supplementary Videos
This directory contains supplementary videos for the anonymous submission.
## S1: TCP4D Tracking
`S1_TCP4D_Tracking/` contains examples of TCP tracking. Each example includes camera-view videos and corresponding point-cloud visualizations. The pcd video shows the predicted point cloud.
## S2: Augmented Data
`S2_augumented_data/` contains example videos organized by source: `DROID/`, `RoboTwin/`, and `Robolab/`.
- `original.mp4` and `source_demo_aloha-agilex.mp4` show source demonstrations.
- Other filenames identify target robot embodiments, including Franka Panda, Jaco, UR5 with WSG, ARX, and Piper. Files marked `cross_embodiment` show cross-embodiment examples.
All videos were processed using our cross-embodiment pipeline.
## S3: Real-World Task Demonstrations
`S3_real_task_demo/` contains videos for two real-world tasks. Filenames beginning with `task1` and `task2` correspond to the respective tasks; files with the `_long` suffix show longer versions.
- `task1_fast.mp4` and `task1_long.mp4`: *placing pens*
- `task2_fast.mp4` and `task2_long.mp4`: *stacking bowls*
Task 1 shows rollouts for *placing pens*, while Task 2 shows rollouts for *stacking bowls*.
TCP4D infer 32 action step for a chunk, and replan when execute 20 steps.
The `_fast` variant uses the cuRobo library with CUDA acceleration for inverse kinematics (IK) planning. For safety, we adjusted the action execution frequency, so its theoretical speed can be higher than shown.
The `_long` variant includes a joint movement penalty in the optimization objective to reduce large changes in joint positions during execution. It is implemented using SciPy without CUDA acceleration.
## S4: Few_shot_Adaptation
These two sets of videos both demonstrate the effect of transfer learning using ten-shot training. The videos are all unseen, only for evaluation..
|