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arXiv Machine Learning

ShuttleArena: Interpretable Self-Play in Physics-Based Badminton

ShuttleArena is a physics‑based singles badminton self‑play environment that integrates continuous shuttle flight, player interception, structured shot generation, and post‑shot recovery. The policy employs role‑conditioned outputs, allowing interpretable tactical probes through masked interception choices for receivers and factorized hitter actions over shot azimuth, elevation, speed, and recovery target. Evaluation with frozen checkpoints, controlled tactical probes, recovery ablations, qualitative rollouts, and a human‑data sanity check demonstrates competitive performance and reveals that learned recovery behavior is critically important for success.

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RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning

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Learning Human-Like Badminton Skills for Humanoid Robots

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Improving Weak World Models Behind Strong Agents in Atari Pong

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AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning

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Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

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