Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

• Published in: IEEE transaction on neural networks and learning systems Vol. 25; no. 6; pp. 1147 - 1160
• Main Authors: Rolf, Matthias, Steil, Jochen J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Actuators; Adaptative systems; Algorithms; Applied sciences; Bellows; Bionic handling assistant (BHA); Computer science; control theory; systems; continuum robot; Control theory. Systems; Data processing. List processing. Character string processing; Exact sciences and technology; Fundamental areas of phenomenology (including applications); goal babbling; inverse kinematics; Inverse problems; Kinematics; Memory organisation. Data processing; Physics; Robot sensing systems; Robotics; Robots; Software; Solid dynamics (ballistics, collision, multibody system, stabilization...); Solid mechanics; Bionic handling assistant (BHA); continuum robot; inverse kinematics; goal babbling; Trunk; On the fly; Dimensionality; Bionics; Solid dynamic; Feedback regulation; Non stationary condition; Non stationary process; Robotics; Inverse problem; Bootstrapping; Closed feedback; Biomimetics; Kinematics; Robot; Intelligent control
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2013.2287890

We present an approach to learn the inverse kinematics of the "bionic handling assistant"-an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse kinematics on the fly. We show the success of the method in extensive real-world experiments on the nonstationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of nonstationary actuation ranges, drifting sensors, and morphological changes. The experiments provide the first substantial quantitative real-world evidence for the success of goal-directed bootstrapping schemes, moreover with the challenge of nonstationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.