Model-free path planning for redundant robots using sparse data from kinesthetic teaching

Abstract

The paper addresses path planning for a redundant robot arm that is maneuvering in confined spaces, where neither an explicit model nor external perception of the possibly frequently changing environment is available. Our approach is rather solely based on data from kinesthetic demonstrations of feasible configurations provided by a user. The key challenge is to create a graph-based representation of the demonstrated free space incrementally and online by means of an specifically tailored instantaneous topological map at runtime. Subsequent application of standard graph-based planning in combination with a learned generalization of the demonstrated redundancy resolution then enables the robot to safely move in the realm of the demonstrated task space areas. This model-free approach greatly enhances configurability and flexibility of the robot for assistance applications, where movement capabilities need to be realized without explicit programming.