Affordance-Based Robot Manipulation with Flow Matching for Activities of Daily Living

This paper introduces a novel framework for assistive robot manipulation that leverages affordance learning through prompt tuning and robot trajectory generation using flow matching, demonstrating superior performance in multimodal action distributions and faster inference compared to traditional methods.