Proactive slip control by learned slip model and trajectory adaptation

Kiyanoush Nazari, Willow Mandil, Amir Masoud Ghalamzan Esfahani

Keywords: Manipulation, Motion planning, Slip control, Motion control

Abstract: This paper presents a novel control approach to dealing with a slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works use increased gripping forces to avoid/control slip. However, this may not be feasible, e.g., because (i) the robot cannot increase the gripping force-- the max gripping force has already applied or (ii) an increased force yields a damaged 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 manipulative movements in real-time may not be feasible, e.g., with the Franka robot. Hence, controlling the slip by changing gripping forces is not an effective control policy in many settings. We propose a novel control approach to slip avoidance including a learned action-conditioned slip predictor and a constrained optimizer avoiding an expected slip given the desired robot actions. We show the effectiveness of this receding horizon controller in a series of test cases in real robot experimentation. Our experimental results show our proposed data-driven predictive controller can control slip for objects unseen in training.

Student First Author: yes

Code: https://github.com/imanlab/proactive-slip-control

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2209.06019/code)