TY - GEN
T1 - Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators
AU - Polydoros, Athanasios S.
AU - Boukas, Evangelos
AU - Nalpantidis, Lazaros
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Inverse dynamics models are applied to a plethora of robot control tasks such as computed-torque control, which are essential for trajectory execution. The analytical derivation of such dynamics models for robotic manipulators can be challenging and depends on their physical characteristics. This paper proposes a machine learning approach for modeling inverse dynamics and provides information about its implementation on a physical robotic system. The proposed algorithm can perform online multi-target learning, thus allowing efficient implementations on real robots. Our approach has been tested both offline, on datasets captured from three different robotic systems and online, on a physical system. The proposed algorithm exhibits state-of-the-art performance in terms of generalization ability and convergence. Furthermore, it has been implemented within ROS for controlling a Baxter robot. Evaluation results show that its performance is comparable to the built-in inverse dynamics model of the robot.
AB - Inverse dynamics models are applied to a plethora of robot control tasks such as computed-torque control, which are essential for trajectory execution. The analytical derivation of such dynamics models for robotic manipulators can be challenging and depends on their physical characteristics. This paper proposes a machine learning approach for modeling inverse dynamics and provides information about its implementation on a physical robotic system. The proposed algorithm can perform online multi-target learning, thus allowing efficient implementations on real robots. Our approach has been tested both offline, on datasets captured from three different robotic systems and online, on a physical system. The proposed algorithm exhibits state-of-the-art performance in terms of generalization ability and convergence. Furthermore, it has been implemented within ROS for controlling a Baxter robot. Evaluation results show that its performance is comparable to the built-in inverse dynamics model of the robot.
UR - http://www.scopus.com/inward/record.url?scp=85041951826&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206344
DO - 10.1109/IROS.2017.8206344
M3 - Article in proceeding
AN - SCOPUS:85041951826
VL - 2017-September
T3 - I E E E International Conference on Intelligent Robots and Systems. Proceedings
SP - 4716
EP - 4722
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
PB - IEEE
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -