Mode Estimation of Probabilistic Hybrid Systems
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Toward Cooperative Team-diagnosis in Multi-robot Systems
International Journal of Robotics Research
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Techniques for robot monitoring and diagnosis have been developed that perform state estimation using probabilistic hybrid discrete/continuous models. Exact inference in hybrid dynamic systems is, in general, intractable. Approximate algorithms are based on either 1) greedy search, as in the case of k-best enumeration or 2) stochastic search, as in the case of Rao-Blackwellised Particle Filtering (RBPF). In this paper we propose a new method for hybrid state estimation. The key insight is that stochastic and greedy search methods, taken together, are often particularly effective in practice. The new method combines the stochastic methods of RBPF with the greedy search of k-best in order to create a method that is effective for a wider range of estimation problems than the individual methods alone. We demonstrate this robustness on a simulated acrobatic robot, and show that this benefit comes at only a small performance penalty.