My research spans topics relating to motion planning and machine learning for robots solving complex tasks.


2017 - Present

2017_bayesian_mp

Bayesian Motion Planning

Motion planning algorithms, in the absence of any prior knowledge, search everywhere for a solution, considering solutions that are almost surely invalid. Can we reduce search time if we are in a Bayesian setting where we have priors on edge validities (e.g. from a database of plausible worlds)? In this work, we explore near Bayes-optimal search strategies for efficiently solving various motion planning problems.
Papers:  IJCAI'18NeurIPS'17ISRR'17

2017 - Present

2017_sail

Learning to Search

How should search algorithms leverage past experience to speed up search? Search algorithms rely on heuristics to balance exploration, i.e., discovering promising new states, and exploitation, i.e., expanding the current best state. We show how such heuristics can be learned by offline imitation of optimal planners.
Papers:  RSS'19CoRL'17

2019 - Present

2019_learningroadmaps

Learning to Sample

How should planners efficiently search over a continuous, high-dimensional state space? Planners typically sample the space to create a roadmap. A desirable roadmap is one that is sparse, allowing for speedy search, with nodes spread out at various bottleneck regions along potential paths. We explore techniques for learning distributions that can be sampled to generate such roadmaps.
Papers:  IROS'19

2015 - 2016

2016_pete

Learning Planner Ensembles

Can we train a learner to select the best planner for a given scenario? We argue that this learning problem is hard - small changes in obstacle configurations dramatically alter planner performance. Instead, we learn to hedge our bets and predict an ensemble of diverse planners that can be run in parallel such that at least one has good performance.
Papers:  ICRA'16ICRA'15