RoboPapers Ep#16 TWIST: Teleoperated Whole-Body Imitation System
Jun 25, 2025
Yanjie Ze, a first-year PhD student at Stanford, dives into the innovative TWIST system, enhancing humanoid robot capabilities through teleoperation. The discussion reveals how human data dramatically improves robot dexterity and addresses challenges in lower body tracking. Yanjie explains the significance of large-scale motion datasets for refining robot movements and explores the complexities of control frameworks. The conversation also highlights advancements in teleoperated robotics, focusing on latency improvements and the potential of full-body engagement for enhanced robotic performance.
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Human Data Powers Dexterity
- Human data is extremely powerful for humanoid robot control but hasn't been fully leveraged by current systems.
- Accurate full-body mapping without information loss can achieve human-level dexterity on robots.
Demonstrating Full-Body Teleoperation
- Yanjie performed whole-body teleoperation showing humanoid tidy a chair and squat deeply.
- The robot demonstrated complex coordinated movements unseen in previous systems.
Limitation of Previous Systems
- Previous systems separated upper and lower body control, often tracking only upper body with VR.
- Accurate lower body tracking is challenging but essential for whole-body coordination.