Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 3, Pages 1479-1488Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3087337
Keywords
Robots; Impedance; Task analysis; Force; Trajectory; Dynamics; Kinematics; Adaptive impedance; force control; human-robot interaction; impedance learning; robotics
Categories
Funding
- National Nature Science Foundation (NSFC) [62003096, 61803103]
- China Postdoctoral Science Foundation [2020M682613]
- Opening Project of Shanghai Robot R&D and Transformation Functional Platform
- Engineering and Physical Sciences Research Council (EPSRC) [EP/S001913]
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This article presents a novel representation model called human-like compliant movement primitives (Hl-CMPs), inspired by a biomimetic adaptive control strategy, which allows a robot to learn human-like compliant behaviors. By learning movement trajectories obtained from human demonstration, the robot can simultaneously learn impedance and force, and represent skills in a parametric manner.
How to enable robotic compliant manipulation has become a critical problem in the robotics field. Inspired by a biomimetic adaptive control strategy, this article presents a novel representation model named human-like compliant movement primitives (Hl-CMPs) which could allow a robot to learn human-like compliant behaviors. The state-of-the-art approaches can hardly learn complete compliant profiles for a specific task. Comparatively, our model can encode task-specific parametric movement trajectories, correspondingly associated with dynamic trajectories including both impedance and feedforward force profiles. The compliant profiles are learned based on a biomimetic control strategy derived from the human motor learning in the muscle space, enabling the robot to simultaneously learn the impedance and the force while executing the movement trajectories obtained from human demonstration. Furthermore, both the kinematic and the dynamic profiles are learned in the parametric space, thus enabling the representation of a skill using corresponding parameters (i.e, task-specific parameters). Hl-CMps can allow the robot to automatically learn compliant behaviors in an online manner after kinematic demonstration. Our approach is validated by an insertion task and a cutting task based on a KUKA LBR iiwa robot.
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