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We are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments.
In Proceedings of the Twenty-First International Conference on Machine Learning , ICML ’04, page 1, 2004
Pieter Abbeel and Andrew Y. Ng · 2004
Earlier work this paper cites.
Maximum margin planning
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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