TY - GEN
T1 - POMHDP
T2 - 29th International Conference on Automated Planning and Scheduling, ICAPS 2019
AU - Kim, Sung Kyun
AU - Salzman, Oren
AU - Likhachev, Maxim
N1 - Publisher Copyright:
© 2019 Association for the Advancement of Artificial Intelligence. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085618336&partnerID=8YFLogxK
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AN - SCOPUS:85085618336
T3 - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
SP - 734
EP - 744
BT - Proceedings of the 29th International Conference on Automated Planning and Scheduling, ICAPS 2019
A2 - Benton, J.
A2 - Lipovetzky, Nir
A2 - Onaindia, Eva
A2 - Smith, David E.
A2 - Srivastava, Siddharth
Y2 - 11 July 2019 through 15 July 2019
ER -