1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
RDA: Reward Design Agent for Reinforcement Learning
Hojoon Lee, Ajay Subramanian, Ben Abbatematteo +4
Reinforcement learning has enabled the acquisition of impressive robotic skills, but typically requires hand-crafted reward functions that are slow to design and difficult to align…
Functional Force-Aware Retargeting from Virtual Human Demos to Soft Robot Policies
Uksang Yoo, Mengjia Zhu, Evan Pezent +8
We introduce SoftAct, a framework for teaching soft robot hands to perform human-like manipulation skills by explicitly reasoning about contact forces. Leveraging immersive virtual…
Stiffness Copilot: An Impedance Policy for Contact-Rich Teleoperation
Yeping Wang, Zhengtong Xu, Pornthep Preechayasomboon +4
In teleoperation of contact-rich manipulation tasks, selecting robot impedance is critical but difficult. The robot must be compliant to avoid damaging the environment, but stiff t…
Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Zhengtong Xu, Yeping Wang, Ben Abbatematteo +4
Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and…