Transformers for One-Shot Visual Imitation

Sudeep Dasari, Abhinav Gupta
Proceedings of the 2020 Conference on Robot Learning, PMLR 155:2071-2084, 2021.

Abstract

Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex semantic knowledge from raw video and efficiently translate that into concrete motor control. Is it possible to give a robot this same capability? Prior research in robot imitation learning has created agents which can acquire diverse skills from expert human operators. However, expanding these techniques to work with a single positive example during test time is still an open challenge. Apart from control, the difficulty stems from mismatches between the demonstrator and robot domains. For example, objects may be placed in different locations (e.g. kitchen layouts are different in every house). Additionally, the demonstration may come from an agent with different morphology and physical appearance (e.g. human), so one-to-one action correspondences are not available. This paper investigates techniques which allow robots to partially bridge these domain gaps, using their past experience. A neural network is trained to mimic ground truth robot actions given context video from another agent, and must generalize to unseen task instances when prompted with new videos during test time. We hypothesize that our policy representations must be both context driven and dynamics aware in order to perform these tasks. These assumptions are baked into the neural network using the Transformers attention mechanism and a self-supervised inverse dynamics loss. Finally, we experimentally determine that our method accomplishes a 2x improvement in terms of task success rate over prior baselines in a suite of one-shot manipulation tasks.

Cite this Paper


BibTeX
@InProceedings{pmlr-v155-dasari21a, title = {Transformers for One-Shot Visual Imitation}, author = {Dasari, Sudeep and Gupta, Abhinav}, booktitle = {Proceedings of the 2020 Conference on Robot Learning}, pages = {2071--2084}, 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/dasari21a/dasari21a.pdf}, url = {https://proceedings.mlr.press/v155/dasari21a.html}, abstract = {Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex semantic knowledge from raw video and efficiently translate that into concrete motor control. Is it possible to give a robot this same capability? Prior research in robot imitation learning has created agents which can acquire diverse skills from expert human operators. However, expanding these techniques to work with a single positive example during test time is still an open challenge. Apart from control, the difficulty stems from mismatches between the demonstrator and robot domains. For example, objects may be placed in different locations (e.g. kitchen layouts are different in every house). Additionally, the demonstration may come from an agent with different morphology and physical appearance (e.g. human), so one-to-one action correspondences are not available. This paper investigates techniques which allow robots to partially bridge these domain gaps, using their past experience. A neural network is trained to mimic ground truth robot actions given context video from another agent, and must generalize to unseen task instances when prompted with new videos during test time. We hypothesize that our policy representations must be both context driven and dynamics aware in order to perform these tasks. These assumptions are baked into the neural network using the Transformers attention mechanism and a self-supervised inverse dynamics loss. Finally, we experimentally determine that our method accomplishes a 2x improvement in terms of task success rate over prior baselines in a suite of one-shot manipulation tasks.} }
Endnote
%0 Conference Paper %T Transformers for One-Shot Visual Imitation %A Sudeep Dasari %A Abhinav Gupta %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-dasari21a %I PMLR %P 2071--2084 %U https://proceedings.mlr.press/v155/dasari21a.html %V 155 %X Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex semantic knowledge from raw video and efficiently translate that into concrete motor control. Is it possible to give a robot this same capability? Prior research in robot imitation learning has created agents which can acquire diverse skills from expert human operators. However, expanding these techniques to work with a single positive example during test time is still an open challenge. Apart from control, the difficulty stems from mismatches between the demonstrator and robot domains. For example, objects may be placed in different locations (e.g. kitchen layouts are different in every house). Additionally, the demonstration may come from an agent with different morphology and physical appearance (e.g. human), so one-to-one action correspondences are not available. This paper investigates techniques which allow robots to partially bridge these domain gaps, using their past experience. A neural network is trained to mimic ground truth robot actions given context video from another agent, and must generalize to unseen task instances when prompted with new videos during test time. We hypothesize that our policy representations must be both context driven and dynamics aware in order to perform these tasks. These assumptions are baked into the neural network using the Transformers attention mechanism and a self-supervised inverse dynamics loss. Finally, we experimentally determine that our method accomplishes a 2x improvement in terms of task success rate over prior baselines in a suite of one-shot manipulation tasks.
APA
Dasari, S. & Gupta, A.. (2021). Transformers for One-Shot Visual Imitation. Proceedings of the 2020 Conference on Robot Learning, in Proceedings of Machine Learning Research 155:2071-2084 Available from https://proceedings.mlr.press/v155/dasari21a.html.

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