10 papers
Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design
Huy Ha, C. Karen Liu, Shuran Song
An often overlooked factor of robot manipulation performance is the embodiment of the robot itself. Motivated by this problem, we study motion-conditioned robot co-design, where th…
ModPack: An Extensible Teleoperation Interface for Bimanual Mobile Manipulation
Joshua Citron, Renee Zbizika, Zeyi Liu +1
Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present ModPack, a modular and extens…
EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
Ryan Punamiya, Simar Kareer, Zeyi Liu +37
Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human data offer a promising alternati…
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
Xiaomeng Xu, Jisang Park, Han Zhang +6
We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free hum…
UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies
Harsh Gupta, Xiaofeng Guo, Huy Ha +6
We introduce UMI-on-Air, a framework for embodiment-aware deployment of embodiment-agnostic manipulation policies. Our approach leverages diverse, unconstrained human demonstration…
Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design
Xiaomeng Xu, Huy Ha, Shuran Song
We present Dynamics-Guided Diffusion Model (DGDM), a data-driven framework for generating task-specific manipulator designs without task-specific training. Given object shapes and…