103 citations · 107 across the 7 of their papers we have counts for
8 papers
EigenDEXplore: Structured Exploration for Dexterous Manipulation with Human Priors
Harsh Gupta, Tyler Ga Wei Lum, Changhao Wang +4
Dexterous manipulation poses a challenging high-dimensional optimization problem, as useful behaviors require coordinated motion across many hand joints. In reinforcement learning…
One Demo is Worth a Thousand Trajectories: Action-View Augmentation for Visuomotor Policies
Chuer Pan, Litian Liang, Dominik Bauer +4
Visuomotor policies for manipulation have demonstrated remarkable potential in modeling complex robotic behaviors, yet minor alterations in the robot's initial configuration and un…
In-the-Wild Compliant Manipulation with UMI-FT
Hojung Choi, Yifan Hou, Chuer Pan +5
Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragili…
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…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
A Study of Perceived Safety for Soft Robotics in Caregiving Tasks
Cosima du Pasquier, Jennifer Grannen, Chuer Pan +6
In this project, we focus on human-robot interaction in caregiving scenarios like bathing, where physical contact is inevitable and necessary for proper task execution because forc…
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
Cheng Chi, Zhenjia Xu, Chuer Pan +5
We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to depl…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…