10 citations · 23 across the 9 of their papers we have counts for
9 papers
Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving
Matt Schmittle, Rohan Baijal, Brian Hou +2
We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously…
Stein Variational Probabilistic Roadmaps
Alexander Lambert, Brian Hou, Rosario Scalise +2
Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately…
Guided Incremental Local Densification for Accelerated Sampling-based Motion Planning
Aditya Mandalika, Rosario Scalise, Brian Hou +2
Sampling-based motion planners rely on incremental densification to discover progressively shorter paths. After computing feasible path between start and goal , the…
Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges
Brian Hou, Sanjiban Choudhury, Gilwoo Lee +2
Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face o…
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Gilwoo Lee, Brian Hou, Sanjiban Choudhury +1
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as Bayesian Reinforcement Lear…
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions
Lerrel Pinto, Aditya Mandalika, Brian Hou +1
This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation. Although reinforcement learning (RL) can learn closed-lo…