6 papers
Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force
Jaden Clark, Changhao Wang, Yihuai Gao +5
Robot manipulation often relies on sensory feedback beyond vision, particularly in contact-rich settings where force, tactile, or audio signals reveal interaction states that are n…
SPIDER: Scalable Physics-Informed Dexterous Retargeting
Chaoyi Pan, Changhao Wang, Haozhi Qi +7
Learning dexterous and agile policy for humanoid and dexterous hand control requires large-scale demonstrations, but collecting robot-specific data is prohibitively expensive. In c…
OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
Jessica Yin, Haozhi Qi, Youngsun Wi +7
Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation.…
Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations
Irmak Guzey, Haozhi Qi, Julen Urain +10
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark si…
Geometric Retargeting: A Principled, Ultrafast Neural Hand Retargeting Algorithm
Zhao-Heng Yin, Changhao Wang, Luis Pineda +4
We introduce Geometric Retargeting (GeoRT), an ultrafast, and principled neural hand retargeting algorithm for teleoperation, developed as part of our recent Dexterity Gen (DexGen)…
DexterityGen: Foundation Controller for Unprecedented Dexterity
Zhao-Heng Yin, Changhao Wang, Luis Pineda +11
Teaching robots dexterous manipulation skills, such as tool use, presents a significant challenge. Current approaches can be broadly categorized into two strategies: human teleoper…