POMHDP: Search-Based Belief Space Planning Using Multiple Heuristics

Sung Kyun Kim, Oren Salzman and Maxim Likhachev
Conference Paper, Proceedings of International Conference on Automated Planning and Scheduling (ICAPS), July, 2019

Download Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Robots operating in the real world encounter substantial uncertainty that cannot be modeled deterministically before the actual execution. This gives rise to the necessity of robust motion planning under uncertainty also known as belief space planning. Belief space planning can be formulated as Partially Observable Markov Decision Processes (POMDPs). However, computing optimal policies for non-trivial POMDPs is computationally intractable. Building upon recent progress from the search community, we propose a novel anytime POMDP solver, Partially Observable Multi-Heuristic Dynamic Programming (POMHDP), that leverages multiple heuristics to efficiently compute high-quality solutions while guaranteeing asymptotic convergence to an optimal policy. Through iterative forward search, POMHDP utilizes domain knowledge to solve POMDPs with specific goals and an infinite horizon. We demonstrate the efficacy of our proposed framework on a real-world, highly-complex, truck unloading application.

author = {Sung Kyun Kim and Oren Salzman and Maxim Likhachev},
title = {POMHDP: Search-Based Belief Space Planning Using Multiple Heuristics},
booktitle = {Proceedings of International Conference on Automated Planning and Scheduling (ICAPS)},
year = {2019},
month = {July},
} 2019-06-11T07:53:13-04:00