Risk Sensitive Particle Filters

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Sebastian Thrun, John Langford, Vandi Verma


We propose a new particle filter that incorporates a model of costs when generating particles. The approach is motivated by the observation that the costs of accidentally not tracking hypotheses might be significant in some areas of state space, and next to irrelevant in others. By incorporat- ing a cost model into particle filtering, states that are more critical to the system performance are more likely to be tracked. Automatic calculation of the cost model is implemented using an MDP value function calcula- tion that estimates the value of tracking a particular state. Experiments in two mobile robot domains illustrate the appropriateness of the approach.