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A Framework for Data-Driven Robotics
We present a framework for data-driven robotics that makes use of a larg...
09/26/2019 ∙ by Serkan Cabi, et al. ∙
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Semi-supervised reward learning for offline reinforcement learning
In offline reinforcement learning (RL) agents are trained using a logged...
12/12/2020 ∙ by Ksenia Konyushkova, et al. ∙
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Interactively shaping robot behaviour with unlabeled human instructions
In this paper, we propose a framework that enables a human teacher to sh...
02/05/2019 ∙ by Anis Najar, et al. ∙
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Generalizing Skills with Semi-Supervised Reinforcement Learning
Deep reinforcement learning (RL) can acquire complex behaviors from low-...
12/01/2016 ∙ by Chelsea Finn, et al. ∙
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Positive-Unlabeled Reward Learning
Learning reward functions from data is a promising path towards achievin...
11/01/2019 ∙ by Danfei Xu, et al. ∙
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Learning Dexterous Manipulation from Suboptimal Experts
Learning dexterous manipulation in high-dimensional state-action spaces ...
10/16/2020 ∙ by Rae Jeong, et al. ∙
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Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Collecting and automatically obtaining reward signals from real robotic ...
10/21/2019 ∙ by Rae Jeong, et al. ∙
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Offline Learning from Demonstrations and Unlabeled Experience
11/27/2020 ∙ by Konrad Zolna, et al. ∙
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Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. However, BC does not effectively leverage what we will refer to as unlabeled experience: data of mixed and unknown quality without reward annotations. This unlabeled data can be generated by a variety of sources such as human teleoperation, scripted policies and other agents on the same robot. Towards data-driven offline robot learning that can use this unlabeled experience, we introduce Offline Reinforced Imitation Learning (ORIL). ORIL first learns a reward function by contrasting observations from demonstrator and unlabeled trajectories, then annotates all data with the learned reward, and finally trains an agent via offline reinforcement learning. Across a diverse set of continuous control and simulated robotic manipulation tasks, we show that ORIL consistently outperforms comparable BC agents by effectively leveraging unlabeled experience.
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