9 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…
Minimalist Compliance Control
Haochen Shi, Songbo Hu, Yifan Hou +3
Compliance control is essential for safe physical interaction, yet its adoption is limited by hardware requirements such as force torque sensors. While recent reinforcement learnin…
In-the-Wild Compliant Manipulation with UMI-FT
Hojung Choi, Yifan Hou, Chuer Pan +5
Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragili…
Locomotion Beyond Feet
Tae Hoon Yang, Haochen Shi, Jiacheng Hu +10
Most locomotion methods for humanoid robots focus on leg-based gaits, yet natural bipeds frequently rely on hands, knees, and elbows to establish additional contacts for stability…
Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
Xiaomeng Xu, Yifan Hou, Chendong Xin +2
We address key challenges in Dataset Aggregation (DAgger) for real-world contact-rich manipulation: how to collect informative human correction data and how to effectively update p…
DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation
Mengda Xu, Han Zhang, Yifan Hou +4
We present DexUMI - a data collection and policy learning framework that uses the human hand as the natural interface to transfer dexterous manipulation skills to various robot han…