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Scaling data-driven robotics with reward sketching and batch reinforcement learning

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arxiv 1909.12200 v3 pith:HVRD2KLN submitted 2019-09-26 cs.RO cs.LG

Scaling data-driven robotics with reward sketching and batch reinforcement learning

Serkan Cabi , Sergio G\'omez Colmenarejo , Alexander Novikov , Ksenia Konyushkova , Scott Reed , Rae Jeong , Konrad Zolna , Yusuf Aytar
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David Budden Mel Vecerik Oleg Sushkov David Barker Jonathan Scholz Misha Denil Nando de Freitas Ziyu Wang
This is my paper
classification cs.RO cs.LG
keywords tasksrewardexperiencerobotbatchdata-drivendatasetdifferent
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.

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Forward citations

Cited by 5 Pith papers

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  3. $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

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  4. Octo: An Open-Source Generalist Robot Policy

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  5. Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

    cs.LG 2020-05 unverdicted novelty 2.0

    Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.