Planning with Continuous Actions in Partially Observable Environments

Abstract— We present a simple randomized POMDP algorithm for planning with continuous actions in partially observable environments. Our algorithm operates on a set of reachable belief points, sampled by letting the robot interact randomly with the environment. We perform value iteration steps, ensuring that in each step the value of all sampled belief points is improved. The idea here is that by sampling actions from a continuous action space we can quickly improve the value of all belief points in the set. We demonstrate the viability of our algorithm on two sets of experiments: one involving an active localization task and one concerning robot navigation in a perceptually aliased office environment.

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Matthijs T. J. Spaan, Nikos A. Vlassis
• Keywords: Belief Points, Continuous Action, ICRA 2005, Reachable Belief Points, Robotics