15 citations · 37 across the 6 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…
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning
Rahul Kumar, Aditya Mandalika, Sanjiban Choudhury +1
We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search,…
Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles
Aditya Mandalika, Sanjiban Choudhury, Oren Salzman +1
Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in t…
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…