Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 38, Issue 2-3, Pages 162-181Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364918780322
Keywords
Planning under uncertainty; POMDP; importance sampling
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Funding
- NUS AcRF Tier 1 grant [R-252-000587-112]
- US Air Force Research Laboratory [FA2386-15-1-4010]
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The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT's performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution.
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