2 citations · 2 across the 4 of their papers we have counts for
4 papers
3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
Nikolaos Gkanatsios, Jiahe Xu, Matthew Bronars +3
We present 3D FlowMatch Actor (3DFA), a 3D policy architecture for robot manipulation that combines flow matching for trajectory prediction with 3D pretrained visual scene represen…
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…
RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution
Wonsuhk Jung, Dennis Anthony, Utkarsh A. Mishra +4
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deplo…
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
Sachit Kuhar, Shuo Cheng, Shivang Chopra +2
Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected…