The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Sensor management using an active sensing approach
Signal Processing
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
The Journal of Machine Learning Research
The factored policy-gradient planner
Artificial Intelligence
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Planning under Uncertainty for Robotic Tasks with Mixed Observability
International Journal of Robotics Research
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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A key challenge to widespread deployment of mobile robots in the real-world is the ability to robustly and autonomously sense the environment and collaborate with teammates. Real-world domains are characterized by partial observability, non-deterministic action outcomes and unforeseen changes, making autonomous sensing and collaboration a formidable challenge. This paper poses vision-based sensing, information processing and collaboration as an instance of probabilistic planning using partially observable Markov decision processes. Reliable, efficient and autonomous operation is achieved using a hierarchical decomposition that includes: (a) convolutional policies to exploit the local symmetry of high-level visual search; (b) adaptive observation functions, policy re-weighting, automatic belief propagation and online updates of the domain map for autonomous adaptation to domain changes; and (c) a probabilistic strategy for a team of robots to robustly share beliefs. All algorithms are evaluated in simulation and on physical robots localizing target objects in dynamic indoor domains.