From the 1 of 10 linked papers with an AI index.
10 papers
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang +3
The paper introduces REGRIND, a simple pipeline that retargets a single human hand‑object demonstration to a robot hand and uses reinforcement learning to train a residual policy t…
ForceBand: Learning Forceful Manipulation with sEMG
Botao He, Zhi Wang, Linna Kuang +8
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories a…
HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
Amber Xie, Haozhi Qi, Dorsa Sadigh
Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty of collecting high-quality data…
Learning Dexterous Manipulation Skills from Imperfect Simulations
Elvis Hsieh, Wen-Han Hsieh, Yen-Jen Wang +4
Reinforcement learning and sim-to-real transfer have made significant progress in dexterous manipulation. However, progress remains limited by the difficulty of simulating complex…
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…
Coordinated Humanoid Manipulation with Choice Policies
Haozhi Qi, Yen-Jen Wang, Toru Lin +4
Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challe…