Publication Details

SELECT * FROM publications WHERE Record_Number=11252
Reference TypeConference Proceedings
Author(s)Liu, Z.; Hitzmann, A.; Ikemoto, S.; Stark, S.; Peters, J.; Hosoda, K.
Year2019
TitleLocal Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm
Journal/Conference/Book TitleIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
AbstractMotor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling has demonstrated successful learning of inverse kinematics (IK) on such systems, and suggests that babbling in the goal space better resolves motor redundancy by learning as few yet efficient sensorimotor mappings as possible. However, for musculoskeletal robot systems, motor redundancy can provide useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the IK of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling guided by Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the goal babbling samples for initialization, such that motor abundance can be queried online for any static goal. Our approach leverages the resolving of redundancies and the efficient guided exploration of motor abundance in two stages of learning, allowing both kinematic accuracy and motor variability at the queried goal. The result shows that local online motor babbling guided by CMA-ES can efficiently explore motor abundance on musculoskeletal robot systems and gives useful insights in terms of muscle stiffness and synergy.
Link to PDFhttps://www.ias.informatik.tu-darmstadt.de/uploads/Team/SvenjaStark/Liu_IROS_2019.pdf

  

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