103 citations · 118 across the 4 of their papers we have counts for
4 papers
RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning
Matthew Lai, Keegan Go, Zhibin Li +4
Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may b…
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Kaylee Burns, Ajinkya Jain, Keegan Go +4
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise moveme…
Efficient Online Learning of Contact Force Models for Connector Insertion
Kevin Tracy, Zachary Manchester, Ajinkya Jain +4
Contact-rich manipulation tasks with stiff frictional elements like connector insertion are difficult to model with rigid-body simulators. In this work, we propose a new approach f…
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…