# Proactive slip control by learned slip model and trajectory adaptation

arXiv: https://arxiv.org/abs/2209.06019 · HTML: https://mlsanity.com/paper/2209.06019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Autonomous Strawberry Picking Robotic System (Robofruit)](https://mlsanity.com/paper/2301.03947/) (2023)
- [Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction](https://mlsanity.com/paper/2303.05393/) (2023)
- [Multi-Modal World Model for Physical Robot Interactions: Simultaneous Visual and Tactile Predictions for Enhanced Accuracy](https://mlsanity.com/paper/2304.11193/) (2023)
- [Single and bi-layered 2-D acoustic soft tactile skin (AST2)](https://mlsanity.com/paper/2401.14292/) (2024)

## Abstract (arXiv)

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.