1 citations · 3 across the 9 of their papers we have counts for
11 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: 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…
Self-Improving Autonomous Underwater Manipulation
Ruoshi Liu, Huy Ha, Mengxue Hou +2
Underwater robotic manipulation faces significant challenges due to complex fluid dynamics and unstructured environments, causing most manipulation systems to rely heavily on human…
Differentiable Robot Rendering
Ruoshi Liu, Alper Canberk, Shuran Song +1
Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them t…
UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers
Huy Ha, Yihuai Gao, Zipeng Fu +2
We introduce UMI-on-Legs, a new framework that combines real-world and simulation data for quadruped manipulation systems. We scale task-centric data collection in the real world u…
Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
Junbang Liang, Ruoshi Liu, Ege Ozguroglu +5
A key challenge in manipulation is learning a policy that can robustly generalize to diverse visual environments. A promising mechanism for learning robust policies is to leverage…
PaperBot: Learning to Design Real-World Tools Using Paper
Ruoshi Liu, Junbang Liang, Sruthi Sudhakar +4
Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is ofte…
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…