Monte Carlo value iteration for continuous-state POMDPS. (English) Zbl 1220.68089
Hsu, David (ed.) et al., Algorithmic foundations of robotics IX. Selected contributions of the ninth international workshop on the algorithmic foundations of robotics, Singapore, December 13–15, 2010. Berlin: Springer (ISBN 978-3-642-17451-3/hbk; 978-3-642-17452-0/ebook). Springer Tracts in Advanced Robotics 68, 175-191 (2011).
Summary: Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuous-state POMDPs. MCVI samples both a robot’s state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.
For the entire collection see [Zbl 1220.68009].
For the entire collection see [Zbl 1220.68009].
MSC:
| 68T40 | Artificial intelligence for robotics |
| 60J28 | Applications of continuous-time Markov processes on discrete state spaces |
