Research
TL;DR: A unified, damage-aware simulation framework for household manipulation that makes physical safety measurable, enabling safer data collection, imitation learning, reinforcement learning, and VLA evaluation.
TL;DR: A system for human-robot collaboration that uses mixed-initiative natural-language dialog so both agents can propose, accept, or reject who completes each step of a task, improving task success and user experience in physical robot trials.
TL;DR: A benchmark for physics-grounded damage detection in manipulation, providing a unified mechanism for quantifying the safety of robot actions. Policies trained with damage-based safety metrics learn safer strategies with substantially lower risk.
TL;DR: A framework that enables robots to safely and autonomously learn multi-step mobile manipulation tasks from a single human video by segmenting, translating, and adapting the demonstrated actions to their own morphology.
* indicates equal contribution