6 citations · 7 across the 3 of their papers we have counts for
7 papers
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…
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
Gilwoo Lee, Sanjiban Choudhury, Brian Hou +1
We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP fram…
Bayesian Policy Optimization for Model Uncertainty
Gilwoo Lee, Brian Hou, Aditya Mandalika +3
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov D…