LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
Sim-to-real transfer of robotic control with dynamics randomization
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Abstract simulators can be grounded to real tasks by making their dynamics history-dependent and correcting them with real data, enabling RL policy transfer.
Event-based perception combined with progressive low-to-high speed training improves robotic table tennis return accuracy by 35.8% using the same number of training episodes.
citing papers explorer
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LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
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Abstract Sim2Real through Approximate Information States
Abstract simulators can be grounded to real tasks by making their dynamics history-dependent and correcting them with real data, enabling RL policy transfer.
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Biologically Inspired Event-Based Perception and Sample-Efficient Learning for High-Speed Table Tennis Robots
Event-based perception combined with progressive low-to-high speed training improves robotic table tennis return accuracy by 35.8% using the same number of training episodes.