NEXT estimates external torques from short free-motion data without hardware sensors and FIRST improves imitation learning by upsampling contact phases, yielding over 17% better task progress on long-horizon manipulation tasks.
In: 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)
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HYMN is a multi-sensor, time-synchronized indoor-outdoor positioning dataset combining five radio technologies (GNSS, UWB, BLE, WiFi, 5G) with total-station ground truth across 48 reference points in an industrial hall.
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FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
NEXT estimates external torques from short free-motion data without hardware sensors and FIRST improves imitation learning by upsampling contact phases, yielding over 17% better task progress on long-horizon manipulation tasks.
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Bimanual Robot Manipulation via Multi-Agent In-Context Learning
HYMN is a multi-sensor, time-synchronized indoor-outdoor positioning dataset combining five radio technologies (GNSS, UWB, BLE, WiFi, 5G) with total-station ground truth across 48 reference points in an industrial hall.