Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior

Michael Beetz and Henrik Grosskreutz

This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, and several modes of their interferences. The main contributions of the paper are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We discuss how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results.


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