Reflexive collision response with virtual skin: Roadmap planning meets reinforcement learning

Prevalent approaches to motion synthesis for complex robots offer either the ability to build up knowledge of feasible actions through exploration, or the ability to react to a changing environment, but not both. This work proposes a simple integration of roadmap planning with reflexive collision response, which allows the roadmap representation to be transformed into a Markov Decision Process. Consequently, roadmap planning is extended to changing environments, and the adaptation of the map can be phrased as a reinforcement learning problem. An implementation of the reflexive collision response is provided, such that the reinforcement learning problem can be studied in an applied setting. The feasibility of the software is analyzed in terms of runtime performance, and its functionality is demonstrated on the iCub humanoid robot.