130 citations · 162 across the 4 of their papers we have counts for
4 papers
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
W. Cannon Lewis, Mark Moll, Lydia E. Kavraki
Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodele…
Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments
Muhayyuddin, Mark Moll, Lydia Kavraki +1
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstruct…
An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
Mark Moll, Ioan A. Sucan, Lydia E. Kavraki
Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many vari…
Experience-Based Planning with Sparse Roadmap Spanners
David Coleman, Ioan A. Sucan, Mark Moll +2
We present an experienced-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environment…