Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:983-993, 2018.

Abstract

Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What happens when they are inaccurate? In this paper, we tackle these questions by proposing a method for learning robotic skills from raw image observations, using only autonomously collected experience. We show that even an imperfect model can complete complex tasks if it can continuously retry, but this requires the model to not lose track of the objective (e.g., the object of interest). To enable a robot to continuously retry a task, we devise a self-supervised algorithm for learning image registration, which can keep track of objects of interest for the duration of the trial. We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation. Our real-world experiments demonstrate that a model trained with 160 robot hours of autonomously collected, unlabeled data is able to successfully perform complex manipulation tasks with a wide range of objects not seen during training.

Cite this Paper

BibTeX
@InProceedings{pmlr-v87-ebert18a, title = {Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning}, author = {Ebert, Frederik and Dasari, Sudeep and Lee, Alex X. and Levine, Sergey and Finn, Chelsea}, booktitle = {Proceedings of The 2nd Conference on Robot Learning}, pages = {983--993}, year = {2018}, editor = {Billard, Aude and Dragan, Anca and Peters, Jan and Morimoto, Jun}, volume = {87}, series = {Proceedings of Machine Learning Research}, month = {29--31 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v87/ebert18a/ebert18a.pdf}, url = {https://proceedings.mlr.press/v87/ebert18a.html}, abstract = {Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What happens when they are inaccurate? In this paper, we tackle these questions by proposing a method for learning robotic skills from raw image observations, using only autonomously collected experience. We show that even an imperfect model can complete complex tasks if it can continuously retry, but this requires the model to not lose track of the objective (e.g., the object of interest). To enable a robot to continuously retry a task, we devise a self-supervised algorithm for learning image registration, which can keep track of objects of interest for the duration of the trial. We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation. Our real-world experiments demonstrate that a model trained with 160 robot hours of autonomously collected, unlabeled data is able to successfully perform complex manipulation tasks with a wide range of objects not seen during training.} }
Endnote
%0 Conference Paper %T Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning %A Frederik Ebert %A Sudeep Dasari %A Alex X. Lee %A Sergey Levine %A Chelsea Finn %B Proceedings of The 2nd Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2018 %E Aude Billard %E Anca Dragan %E Jan Peters %E Jun Morimoto %F pmlr-v87-ebert18a %I PMLR %P 983--993 %U https://proceedings.mlr.press/v87/ebert18a.html %V 87 %X Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What happens when they are inaccurate? In this paper, we tackle these questions by proposing a method for learning robotic skills from raw image observations, using only autonomously collected experience. We show that even an imperfect model can complete complex tasks if it can continuously retry, but this requires the model to not lose track of the objective (e.g., the object of interest). To enable a robot to continuously retry a task, we devise a self-supervised algorithm for learning image registration, which can keep track of objects of interest for the duration of the trial. We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation. Our real-world experiments demonstrate that a model trained with 160 robot hours of autonomously collected, unlabeled data is able to successfully perform complex manipulation tasks with a wide range of objects not seen during training.
APA
Ebert, F., Dasari, S., Lee, A.X., Levine, S. & Finn, C.. (2018). Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning. Proceedings of The 2nd Conference on Robot Learning, in Proceedings of Machine Learning Research 87:983-993 Available from https://proceedings.mlr.press/v87/ebert18a.html.

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