Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations
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1703.05623.pdf
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2.57 MB
Format
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Checksum (MD5)
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Author(s)
Amato, Christopher
Vian, John
Omidshafiei, Shayegan
Liu, Shih-Yuan
Everett, Michael F
Lopez, Brett Thomas
Liu, Miao
How, Jonathan P
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Omidshafiei, Shayegan, Shih-Yuan Liu, Michael Everett, Brett T. Lopez, Christopher Amato, Miao Liu, Jonathan P. How, and John Vian. “Semantic-Level Decentralized Multi-Robot Decision-Making Using Probabilistic Macro-Observations.” 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, Singapore, Singapore, 2017.
Version
Original manuscript
Abstract
Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sources in a robot's observation model. This is important not only for low-level observations (e.g., accelerom-eter data), but also for high-level observations such as semantic object labels. This paper formalizes the concept of macro-observations in Decentralized Partially Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable semantic-level multi-robot decision making. A hierarchical Bayesian approach is used to model noise statistics of low-level classifier outputs, while simultaneously allowing sharing of domain noise characteristics between classes. Classification accuracy of the proposed macro-observation scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed existing methods. The macro-observation scheme is then integrated into a Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic quadrotors in a challenging domain where noise-agnostic filtering fails. To the best of our knowledge, this is the first demonstration of a real-time, convolutional neural net-based classification framework running fully onboard a team of quadrotors in a multi-robot decision-making domain.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989107
