Accelerated Sensorimotor Learning of Compliant Movement Primitives

• Published in: IEEE transactions on robotics Vol. 34; no. 6; pp. 1636 - 1642
• Main Authors: Petric, Tadej, Gams, Andrej, Colasanto, Luca, Ijspeert, Auke J., Ude, Ales
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous learning; Coding; compliant movement primitives (CMPs); Human behavior; Human performance; internal dynamic models; Kinematics; Learning; Learning systems; Movement; Robot sensing systems; Task analysis; Trajectories; Trajectory
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2018.2861921

Autonomous trajectory generation through generalization requires a database of motion, which can be difficult and time consuming to obtain. In this paper, we propose a method for autonomous expansion of a database for the generation of compliant and accurate motion, achieved through the framework of compliant movement primitives (CMPs). These combine task-specific kinematic and corresponding feed-forward dynamic trajectories. The framework allows for generalization and modulation of dynamic behavior. Inspired by human sensorimotor learning abilities, we propose a novel method that can autonomously learn task-specific torque primitives (TPs) associated to given kinematic trajectories, encoded as dynamic movement primitives. The proposed algorithm is completely autonomous, and can be used to rapidly generate and expand the CMP database. Since CMPs are parameterized, statistical generalization can be used to obtain an initial TP estimate of a new CMP. Thereby, the learning rate of new CMPs can be significantly improved. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a peg-in-hole task shows fast TP acquisition and accurate generalization estimates in real-world scenarios.