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State abstraction is an effective technique for planning in robotics environments with continuous states and actions, long task horizons, and sparse feedback.
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Efficient learning of relational models for sequential decision making
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David Krueger, Jan Leike, Owain Evans, and John Salvatier · 2011
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Christophe Rodrigues, Pierre Gérard, Céline Rouveirol, and Henry Soldano · 2011
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Burr Settles · 2011
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Deep affordance foresight: Planning through what can be done in the future, 2020
Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, and Li Fei-Fei · 2011
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Exploration in relational domains for model-based reinforcement learning
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Learning grounded relational symbols from continuous data for abstract reasoning, 2013
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Active learning for teaching a robot grounded relational symbols
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Diederik P. Kingma and Jimmy Ba · 2014
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Robots that use language
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Model primitives for hierarchical lifelong reinforcement learning
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Learning compositional models of robot skills for task and motion planning
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Do as I can, not as I say: Grounding language in robotic affordances
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