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  2. S1_TCP4D_Tracking/.DS_Store +0 -0
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  20. S2_augumented_data/DROID/.DS_Store +0 -0
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  22. S2_augumented_data/DROID/03_Sat_Aug_12_remove_cables/original.mp4 +3 -0
  23. S2_augumented_data/DROID/03_Sat_Aug_12_remove_cables/ur5_wsg.mp4 +3 -0
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README.md ADDED
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+ # Supplementary Videos
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+
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+ This directory contains supplementary videos for the anonymous submission.
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+
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+ ## S1: TCP4D Tracking
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+
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+ `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.
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+
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+ ## S2: Augmented Data
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+
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+ `S2_augumented_data/` contains example videos organized by source: `DROID/`, `RoboTwin/`, and `Robolab/`.
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+
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+ - `original.mp4` and `source_demo_aloha-agilex.mp4` show source demonstrations.
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+ - Other filenames identify target robot embodiments, including Franka Panda, Jaco, UR5 with WSG, ARX, and Piper. Files marked `cross_embodiment` show cross-embodiment examples.
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+
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+ All videos were processed using our cross-embodiment pipeline.
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+
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+ ## S3: Real-World Task Demonstrations
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+
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+ `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.
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+
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+ - `task1_fast.mp4` and `task1_long.mp4`: *placing pens*
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+ - `task2_fast.mp4` and `task2_long.mp4`: *stacking bowls*
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+
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+ Task 1 shows rollouts for *placing pens*, while Task 2 shows rollouts for *stacking bowls*.
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+
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+ TCP4D infer 32 action step for a chunk, and replan when execute 20 steps.
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+
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+ 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.
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+
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+ 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.
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+
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+ ## S4: Few_shot_Adaptation
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+
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+ These two sets of videos both demonstrate the effect of transfer learning using ten-shot training. The videos are all unseen, only for evaluation..
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