activity
20192021
most citedLearning a Decentralized Multi-arm Motion Planner

19 citations · 20 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG20211 cited

How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?

Jingxi Xu, Bruce Lee, Nikolai Matni +1

The difficulty of optimal control problems has classically been characterized in terms of system properties such as minimum eigenvalues of controllability/observability gramians. W…

cs.RO2021

Dynamic Grasping with Reachability and Motion Awareness

Iretiayo Akinola, Jingxi Xu, Shuran Song +1

Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning n…

cs.RO202019 cited

Learning a Decentralized Multi-arm Motion Planner

Huy Ha, Jingxi Xu, Shuran Song

We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robot systems have relied on centralized motion planners, whos…

cs.RO2019

Accelerated Robot Learning via Human Brain Signals

Iretiayo Akinola, Zizhao Wang, Junyao Shi +6

In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning…

cs.RO2019

Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

David Watkins-Valls, Jingxi Xu, Nicholas Waytowich +1

We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real e…