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 uses 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 and constrained reinforcement learning for robust motion control 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.

中文翻译：

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学习腿部纵器的协调羽毛球技巧

协调下肢和上肢之间的运动以及使肢体控制与感知保持一致是机器人技术面临的重大挑战，尤其是在动态环境中。为此，我们引入了一种使腿式移动机械手能够打羽毛球的方法，这是一项需要精确协调感知、运动和手臂摆动的任务。我们提出了一种统一的基于强化学习的全身视觉运动技能控制策略，涉及所有自由度，以实现有效的羽毛球跟踪和打击。此策略由使用真实摄像机数据的感知噪声模型提供信息，允许在模拟和部署之间保持一致的感知误差级别，并鼓励学习主动感知行为。我们的方法包括羽毛球预测模型和约束强化学习，以实现稳健的运动控制，以增强部署准备。在各种环境中的大量实验结果验证了机器人预测羽毛球轨迹、有效导航服务区以及对人类玩家执行精确打击的能力，证明了在复杂和动态的运动场景中使用腿式移动机械手的可行性。