Jul 5, 2023 · We present a framework for modeling the coefficient of friction \mu as a distribution rather than a constant, and show how this distribution can be narrowed.
Aug 25, 2023 · Application of this model to machine learning has the potential to enhance reinforcement learning and sim-to-real transfer by providing more ...
Reliable grasping and manipulation in daily tasks and unstructured environments require accurate contact modeling and grasp stability estimation.
Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation. Z Liu, RD Howe. IEEE Robotics and Automation Letters, 2023. 7, 2023.
Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation. Liu, Zixi;Howe, Robert D. IEEE robotics and ...
Liu. Z., and Howe, R. D.. Beyond Coulomb Stochastic Friction Models for Practical Grasping and Manipulation IEEE Robotics and Automation Letters, vol. 8, no.
The proposed framework could be used for real-time, closed-loop wearable control during real-world locomotion. Read more. Beyond Coulomb: Stochastic Friction ...
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This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges ...
Contact Modeling. Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation, Zixi Liu, Robert D. Howe, Contact Modeling. LEAGUE: Guided ...
Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation · Zixi LiuR. Howe. Engineering, Physics. IEEE Robotics and Automation Letters.