用游戏机舞蹈游戏测试机器人动作，实现低成本真实对比。
Switch4EAI: Leveraging Console Game Platform for Benchmarking Robotic Athletics
- 用任天堂Switch游戏《Just Dance》捕捉动作并转给机器人执行
- 在单位人形机器人上实现与真人玩家相当的舞蹈同步率
- 为具身智能提供可复现、物理真实的基准测试方案
近期全身机器人控制技术进步使人形和腿式机器人能完成越来越敏捷协调的动作。然而，缺乏在真实环境中评估机器人运动能力并与人类直接比较的标准基准。本文提出Switch4EAI（Switch-for-Embodied-AI），一种低成本且易于部署的管道，利用动作感应游戏平台评估全身机器人控制策略。以任天堂Switch的《Just Dance》为例，系统捕捉、重建并重定向游戏中编舞供机器人执行。我们在Unitree G1人形机器人上使用开源全身控制器验证该系统，建立了机器人性能相对于真人玩家的定量基准。文中讨论了这些结果，表明商用游戏平台可作为物理真实基准的可行性，并激励未来在具身智能评测方面的工作。
原文摘要 · Abstract (English)
Recent advances in whole-body robot control have enabled humanoid and legged robots to execute increasingly agile and coordinated movements. However, standardized benchmarks for evaluating robotic athletic performance in real-world settings and in direct comparison to humans remain scarce. We present Switch4EAI(Switch-for-Embodied-AI), a low-cost and easily deployable pipeline that leverages motion-sensing console games to evaluate whole-body robot control policies. Using Just Dance on the Nintendo Switch as a representative example, our system captures, reconstructs, and retargets in-game choreography for robotic execution. We validate the system on a Unitree G1 humanoid with an open-source whole-body controller, establishing a quantitative baseline for the robot's performance against a human player. In the paper, we discuss these results, which demonstrate the feasibility of using commercial games platform as physically grounded benchmarks and motivate future work to for benchmarking embodied AI.
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