Proactive slip control by learned slip model and trajectory adaptation

Kiyanoush Nazari, Willow Mandil, Amir Masoud Ghalamzan Esfahani
Proceedings of The 6th Conference on Robot Learning, PMLR 205:751-761, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-nazari23a, title = {Proactive slip control by learned slip model and trajectory adaptation}, author = {Nazari, Kiyanoush and Mandil, Willow and Esfahani, Amir Masoud Ghalamzan}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {751--761}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/nazari23a/nazari23a.pdf}, url = {https://proceedings.mlr.press/v205/nazari23a.html}, 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. } }
Endnote
%0 Conference Paper %T Proactive slip control by learned slip model and trajectory adaptation %A Kiyanoush Nazari %A Willow Mandil %A Amir Masoud Ghalamzan Esfahani %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-nazari23a %I PMLR %P 751--761 %U https://proceedings.mlr.press/v205/nazari23a.html %V 205 %X 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.
APA
Nazari, K., Mandil, W. & Esfahani, A.M.G.. (2023). Proactive slip control by learned slip model and trajectory adaptation. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:751-761 Available from https://proceedings.mlr.press/v205/nazari23a.html.

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