2 citations · 2 across the 5 of their papers we have counts for
5 papers
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
Refinery: Active Fine-tuning and Deployment-time Optimization for Contact-Rich Policies
Bingjie Tang, Iretiayo Akinola, Jie Xu +6
Simulation-based learning has enabled policies for precise, contact-rich tasks (e.g., robotic assembly) to reach high success rates (~80%) under high levels of observation noise an…
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Michael Noseworthy, Bingjie Tang, Bowen Wen +7
We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, F…
AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries
Bingjie Tang, Iretiayo Akinola, Jie Xu +7
Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-t…
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
Motoya Ohnishi, Iretiayo Akinola, Jie Xu +2
Path signatures have been proposed as a powerful representation of paths that efficiently captures the path's analytic and geometric characteristics, having useful algebraic proper…