Learning Vision-based Reactive Policies for Obstacle Avoidance

Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer, Sami Haddadin
Proceedings of the 2020 Conference on Robot Learning, PMLR 155:2040-2054, 2021.

Abstract

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate while maintaining closed-loop responsiveness required for critical applications like human-robot interaction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v155-aljalbout21a, title = {Learning Vision-based Reactive Policies for Obstacle Avoidance}, author = {Aljalbout, Elie and Chen, Ji and Ritt, Konstantin and Ulmer, Maximilian and Haddadin, Sami}, booktitle = {Proceedings of the 2020 Conference on Robot Learning}, pages = {2040--2054}, year = {2021}, editor = {Kober, Jens and Ramos, Fabio and Tomlin, Claire}, volume = {155}, series = {Proceedings of Machine Learning Research}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v155/aljalbout21a/aljalbout21a.pdf}, url = {https://proceedings.mlr.press/v155/aljalbout21a.html}, abstract = {In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate while maintaining closed-loop responsiveness required for critical applications like human-robot interaction.} }
Endnote
%0 Conference Paper %T Learning Vision-based Reactive Policies for Obstacle Avoidance %A Elie Aljalbout %A Ji Chen %A Konstantin Ritt %A Maximilian Ulmer %A Sami Haddadin %B Proceedings of the 2020 Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2021 %E Jens Kober %E Fabio Ramos %E Claire Tomlin %F pmlr-v155-aljalbout21a %I PMLR %P 2040--2054 %U https://proceedings.mlr.press/v155/aljalbout21a.html %V 155 %X In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate while maintaining closed-loop responsiveness required for critical applications like human-robot interaction.
APA
Aljalbout, E., Chen, J., Ritt, K., Ulmer, M. & Haddadin, S.. (2021). Learning Vision-based Reactive Policies for Obstacle Avoidance. Proceedings of the 2020 Conference on Robot Learning, in Proceedings of Machine Learning Research 155:2040-2054 Available from https://proceedings.mlr.press/v155/aljalbout21a.html.

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