Switch4EAI: 家庭用ゲーム機をロボット運動能力のベンチマークに活用
Switch4EAI: Leveraging Console Game Platform for Benchmarking Robotic Athletics
Nintendo Switchの『Just Dance』のようなモーションセンシングゲームを利用して、ヒューマノイドロボットの全身制御ポリシーを評価する低コストで導入しやすいベンチマーク手法を提案し、Unitree G1で人間との比較定量評価を行った。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Tianyu Li, Jeonghwan Kim, Wontaek Kim, Donghoon Baek, Seungeun Rho, Sehoon Ha
分類: cs.RO
原文アブストラクト
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.
