brandonyang/dual-lidar-combined-filtered-long-gripper
Combined filtered dual-LiDAR UMI demonstrations Observation-only LeRobot v3 derivative of brandonyang/dual-lidar-umi, brandonyang/dual-lidar-umi-relative. It contains 182 demonstrations (179951 frames) accepted by the continuous bimanual YAM replayability pipeline. The 12-D observation.state contains the smoothed, trajectory-optimized YAM-achievable path in the zero-origin UMI Cartesian convention. Raw UMI gripper widths remain as separate observations. The two original UMI… See the full description on the dataset page: https://huggingface.co/datasets/brandonyang/dual-lidar-combined-filtered-long-gripper.
Combined filtered dual-LiDAR UMI demonstrations
Observation-only LeRobot v3 derivative of brandonyang/dual-lidar-umi, brandonyang/dual-lidar-umi-relative. It contains 182 demonstrations (179951 frames) accepted by the continuous bimanual YAM replayability pipeline.
The 12-D observation.state contains the smoothed, trajectory-optimized YAM-achievable path in the zero-origin UMI Cartesian convention. Raw UMI gripper widths remain as separate observations. The two original UMI videos, timestamps, frame cadence, and task are preserved; action is intentionally absent.
IK and FK use the current BiYAM-pinned I2RT YAM model with the custom 220 mm linear gripper and UMI-compatible grasp frame.
Both UMI trajectories are expressed relative to their own first pose, so both begin at zero. See meta/filtering.json for pinned source revisions, gates, provenance, and the output-to-source episode map. Collision checks are sampled-capsule proxies; validate the exact scene before hardware playback.