Proactive slip control by learned slip model and trajectory adaptation

Nazari, Kiyanoush, Mandil, Willow and Ghalamzan Esfahani, Amir (2022) Proactive slip control by learned slip model and trajectory adaptation. In: 6th Conference on Robot Learning, 14th-16th December 2022, Auckland, New Zealand.

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Proactive slip control by learned slip model and trajectory adaptation
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CoRL_Online_Trajectory_Adaptation_for_Slip_avoidance (3).pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


This paper presents a novel control approach to dealing with object slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works increase grip force to avoid/control slip. However, this may not be feasible when (i) the robot cannot increase the gripping force– the max gripping force is already applied or (ii) in- creased force damages the grasped object, such as soft fruit. Moreover, the robot fixes the gripping force when it forms a stable grasp on the surface of an object, and changing the gripping force during real-time manipulation may not be an effective control policy. We propose a novel control approach to slip avoidance including a learned action-conditioned slip predictor and a constrained optimiser avoiding a predicted slip given a desired robot action. We show the effectiveness of the proposed trajectory adaptation method with the receding horizon controller with a series of real-robot test cases. Our experimental results show our proposed data-driven predictive controller can control slip for objects unseen in training.

Keywords:Robotic, manipulation, Predictive Control
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
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ID Code:52220
Deposited On:01 Nov 2022 16:05

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