Planning and acting in partially observable stochastic domains
Artificial Intelligence
Robot Motion Planning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Policy-contingent abstraction for robust robot control
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Decentralized multi-robot cooperation with auctioned POMDPs
International Journal of Robotics Research
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
Integrated task and motion planning in belief space
International Journal of Robotics Research
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Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically improved the speed of POMDP planning, enabling us to handle moderately complex robotic tasks. However, robot motion planning tasks with long time horizons remains a severe obstacle for even the fastest point-based POMDP solvers today. This paper proposes Milestone Guided Sampling (MiGS), a new point-based POMDP solver, which exploits state space information to reduce effective planning horizons. MiGS samples a set of points, called milestones, from a robot芒聙聶s state space and constructs a simplified representation of the state space from the sampled milestones. It then uses this representation of the state space to guide sampling in the belief space and tries to capture the essential features of the belief space with a small number of sampled points. Preliminary results are very promising. We tested MiGS in simulation on several difficult POMDPs that model distinct robotic tasks with long time horizons in both 2-D and 3-D environments. These POMDPs are impossible to solve with the fastest point-based solvers today, but MiGS solved them in a few minutes.