15 citations · 16 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
Michael Drolet, Simon Stepputtis, Siva Kailas +4
Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance h…
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…
RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
Priya Sundaresan, Quan Vuong, Jiayuan Gu +10
Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over…
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
Jianlan Luo, Zheyuan Hu, Charles Xu +7
In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real…
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…