Date of Original Version
Abstract or Description
Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence of actions, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well even with only one step lookahead. Our work first explains this high performance -- we note that a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. Recently developed notions of adaptive submodularity enable guarantees for a greedy algorithm in the online setting. We develop new methods within this framework, enabling us to provide guarantees compared to the optimal online policy, as well as exploit additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.
Proceedings of IEEE International Conference on Robotics and Automation (ICRA).