A L-MCRS dynamics approximation by ELM for Reinforcement Learning
Autonomous task learning for Linked Multicomponent Robotic Systems (L-MCRS) is an open research issue. Pilot studies applying Reinforcement Learning (RL) on Single Robot Hose Transport (SRHT) task need extensive simulations of the L-MCRS involved in the task. The Geometrically Exact Dynamic Spline (GEDS) simulator used for the accurate simulation of the dynamics of the overall system is a time expensive process, so that it is infeasible to carry out extensive learning experiments based on it. In this paper we address the problem of learning the dynamics of the L-MCRS encapsulated on the GEDS simulator using an Extreme Learning Machine (ELM) approach. Profiting from the adaptability and flexibility of the ELMs, we have formalized the problem of learning the hose geometry as a multi-variate regression problem. Empirical evaluation of this strategy achieves remarkable accurate approximation results.
reinforcement learning linked multicomponent robotic systems extreme learning machines hose control