5 papers
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…
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…
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…
Skill Generalization with Verbs
Rachel Ma, Lyndon Lam, Benjamin A. Spiegel +6
It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a give…