A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper proposes a method to transfer exploratory policies from simulation to real-world reinforcement learning, overcoming the sim-to-real gap even when direct transfer fails.
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This paper presents a Real-to-Sim-to-Real framework that achieves RGB-only sim-to-real policy transfer for visual robot navigation and locomotion.
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This paper proposes closing the sim-to-real loop by adapting simulation randomization based on real-world experience to improve robot learning transfer.
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This paper presents a comprehensive recipe for applying sim-to-real reinforcement learning to vision-based dexterous manipulation on humanoid robots, synthesizing practical techniques and insights.
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This paper introduces a sim-to-real approach using randomized-to-canonical adaptation networks to transfer a vision-based closed-loop grasping reinforcement learning agent from simulation to a real robot.
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A survey of sim-to-real methods in reinforcement learning, focusing on progress, prospects, and challenges, with an emphasis on foundation models.
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A survey of sim-to-real transfer techniques in deep reinforcement learning for robotics, focusing on end-to-end approaches and relevant applications.
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A review of systematic approaches to randomized simulations for robot learning, building on early methods to advance sim-to-real transfer.