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.
Full content URL: https://openreview.net/forum?id=UWTP_JvSug
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Item Type: | Conference or Workshop contribution (Paper) |
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Item Status: | Live Archive |
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Abstract
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.
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