Bridging the Gap of Abstraction for Probabilistic Decision Making on a Multi-Modal Service Robot

This paper proposes a decision making and control supervision system for a multi-modal service robot. With partially observable Markov decision processes (POMDPs) utilized for scenario level decision making, the robot is able to deal with uncertainty in both observation and environment dynamics and can balance multiple, conflicting goals. By using a flexible task sequencing system for fine grained robot component coordination, complex sub-activities, beyond the scope of current POMDP solutions, can be performed. The sequencer bridges the gap of abstraction between abstract POMDP models and the physical world concerning actions, and in the other direction multi-modal perception is filtered while preserving measurement uncertainty and model-soundness. A realistic scenario for an autonomous, anthropomorphic service robot, including the modalities of mobility, multi-modal human-robot interaction and object grasping, has been performed robustly by the system for several hours. The proposed filter-POMDP reasoner is compared with classic POMDP as well as MDP decision making and a baseline finite state machine controller on the physical service robot, and the experiments exhibit the characteristics of the different algorithms.