让机器人像人一样打羽毛球，实现全身协调与实时感知。
Learning coordinated badminton skills for legged manipulators
- 用强化学习统一控制腿部移动和手臂击球，实现全身体感联动。
- 在真实环境中成功预测羽毛球轨迹并精准击打人类对手。
- 结合真实摄像头噪声模型，提升仿真与实机部署一致性。
在动态环境中协调机器人的下肢与上肢运动，并使肢体控制与感知对齐是机器人领域的重大挑战。为此，我们提出一种方法，使具备腿式移动能力的机械臂能够打羽毛球——这一任务要求精确协调感知、移动与手臂挥动。我们设计了一种基于强化学习的统一控制策略，涵盖所有自由度，实现有效的羽毛球追踪与击打。该策略基于利用真实摄像头数据构建的感知噪声模型，确保仿真与实际部署中的感知误差水平一致，并促进学习主动感知行为。方法包含羽毛球轨迹预测模型、约束强化学习以实现鲁棒运动控制，以及系统辨识技术提升部署准备度。在多种环境下的大量实验验证了机器人能准确预测羽毛球轨迹，有效导航发球区，并对人类玩家执行精准击打，证明了腿式移动机械臂在复杂动态体育场景中的可行性。
原文摘要 · Abstract (English)
Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To this end, we introduce an approach for enabling legged mobile manipulators to play badminton, a task that requires precise coordination of perception, locomotion, and arm swinging. We propose a unified reinforcement learning-based control policy for whole-body visuomotor skills involving all degrees of freedom to achieve effective shuttlecock tracking and striking. This policy is informed by a perception noise model that utilizes real-world camera data, allowing for consistent perception error levels between simulation and deployment and encouraging learned active perception behaviors. Our method includes a shuttlecock prediction model, constrained reinforcement learning for robust motion control, and integrated system identification techniques to enhance deployment readiness. Extensive experimental results in a variety of environments validate the robot's capability to predict shuttlecock trajectories, navigate the service area effectively, and execute precise strikes against human players, demonstrating the feasibility of using legged mobile manipulators in complex and dynamic sports scenarios.
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