Probabilistic inference for structured planning in robotics

Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially depends on exploiting this structure. We propose a new approach to planning in robotics based on probabilistic inference. The method uses structured Dynamic Bayesian Networks to represent the scenario and efficient inference techniques (loopy belief propagation) to solve planning problems. In principle, any kind of factored or hierarchical state representations can be accounted for. We demonstrate the approach on reaching tasks under collision avoidance constraints with a humanoid upper body.