Fast discovery of influential outcomes for risk-aware MPDM

Dhanvin Mehta, Gonzalo Ferrer, Edwin Olson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

In the Multi-Policy Decision Making (MPDM) framework, a robot's policy is elected by sampling from the distribution of current states, predicting future outcomes through forward simulation, and selecting the policy with the best expected performance. Electing the best plan depends on sampling initial conditions with influential (very high costs) outcomes. Discovering these configurations through random sampling may require drawing many samples, which becomes a performance bottleneck. In this paper, we describe a risk-aware approach which augments this sampling with an optimization process that helps discover those influential outcomes. We describe how we overcome several practical difficulties with this approach, and demonstrate significant performance improvements on a real robot platform navigating a semi-crowded, highly dynamic environment.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6210-6216
Number of pages7
ISBN (Electronic)9781509046331
DOIs
Publication statusPublished - 21 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

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