Proactive slip control by learned slip model and trajectory adaptation
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 r...
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Zusammenfassung: | 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) increased 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
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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DOI: | 10.48550/arxiv.2209.06019 |