• Corpus ID: 237370930

Back to Reality for Imitation Learning

@article{Johns2021BackTR,
  title={Back to Reality for Imitation Learning},
  author={Edward Johns},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.12867}
}
Imitation learning, and robot learning in general, emerged due to break1 throughs in machine learning, rather than breakthroughs in robotics. As such, 2 evaluation metrics for robot learning are deeply rooted in those for machine learn3 ing. In this paper, we expose a worrying trend that this has led to, with a meta4 analysis of imitation learning papers accepted at CoRL 2020. We show that tra5 ditional evaluation metrics, which only encourage data efficiency, do not con6 sider how the robot… 
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TLDR
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TLDR
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  • Edward Johns
  • Computer Science
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  • 2021
We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any prior knowledge of…
Learning Multi-Stage Tasks with One Demonstration via Self-Replay
In this work, we introduce a novel method to learn everyday-like multistage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the recent…
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