Thursday, February 6, 2014

Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

Finally. Our results on Goal Babbling for the control of the Bionic Handling Assistant now got published (so far as "early access", PDF see below) in IEEE Trans. Neural Networks and Learning Systems. The paper first describes a lot of details about the platform and its specific difficulties both for learning and control. Most significantly: its narrow and ever-changing actuation ranges. The paper then digs into Goal Babbling and describes quite extensive experiments of the physical robot platform. We show that learning quickly give useful results consistently across several trials. We introduce a novel combination of learning inverse models for feedforward control with Goal Babbling, and a feedback control strategy that allows to eliminate large portions of residual control errors. We show that this strategy reaches accuracies very close to the robots repetition accuracy of the robot (essentially, you can't get better than that). The paper closes with some BHA-simulation experiments showing how Goal Babbling can deal with various cases of non-stationary behavior on the robot (including morphological growth).
  • Rolf, M., and J.J. Steil, "Efficient exploratory learning of inverse kinematics on a bionic elephant trunk", IEEE Trans. Neural Networks and Learning Systems. (pdf)
Abstract — 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 non-stationary system behavior. We utilize 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 non-stationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of non-stationary 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 non-stationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.
The following video gives some impression of the results:
The use of the learned model allows quite fast movements considering the long delays in the actuation. The very last sequence shows the use of the additional feedback controller to grasp the cup.

No comments:

Post a Comment