15 citations · 37 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…
Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges
Aditya Mandalika, Oren Salzman, Siddhartha Srinivasa
Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the…