🌐 Website Introduction

This website presents the research outcomes of our group to demonstrate progress toward our funded project goal: using physical simulation to detect and reason about the affordances of objects.

Our central concept is affordance imagination — enabling robots to mentally simulate possible interactions with previously unseen objects. By integrating physics-based reasoning, geometric analysis, and learning methods (from demonstrations and large language models), our robots can classify novel objects, predict functional poses, and execute manipulation strategies without relying on massive amounts of training data.

The works presented here illustrate how affordance imagination bridges the gap between theory and practice: from seating a teddy bear on a previously unseen chair, to predicting hanging poses of tools, to capping containers, to leveraging LLMs for task decomposition. Together, these efforts chart a path toward safe, generalizable, and intelligent robot interaction in household and healthcare environments.