Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
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2025
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| author | Kim, Jeonghwan Kim, Wontaek Lu, Yidan Cheng, Jin Zargarbashi, Fatemeh Zeng, Zicheng Qi, Zekun Dou, Zhiyang Sontakke, Nitish Baek, Donghoon Ha, Sehoon Li, Tianyu |
| author_facet | Kim, Jeonghwan Kim, Wontaek Lu, Yidan Cheng, Jin Zargarbashi, Fatemeh Zeng, Zicheng Qi, Zekun Dou, Zhiyang Sontakke, Nitish Baek, Donghoon Ha, Sehoon Li, Tianyu |
| contents | Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for evaluating these capabilities in real-world settings, and in direct comparison to humans, remain scarce. Existing evaluations often rely on pre-collected human motion datasets or simulation-based experiments, which limit reproducibility, overlook hardware factors, and hinder fair human-robot comparisons. We present Switch-JustDance, a low-cost and reproducible benchmarking pipeline that leverages motion-sensing console games, Just Dance on the Nintendo Switch, to evaluate robot whole-body control. Using Just Dance on the Nintendo Switch as a representative platform, Switch-JustDance converts in-game choreography into robot-executable motions through streaming, motion reconstruction, and motion retargeting modules and enables users to evaluate controller performance through the game's built-in scoring system. We first validate the evaluation properties of Just Dance, analyzing its reliability, validity, sensitivity, and potential sources of bias. Our results show that the platform provides consistent and interpretable performance measures, making it a suitable tool for benchmarking embodied AI. Building on this foundation, we benchmark three state-of-the-art humanoid whole-body controllers on hardware and provide insights into their relative strengths and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17925 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game Kim, Jeonghwan Kim, Wontaek Lu, Yidan Cheng, Jin Zargarbashi, Fatemeh Zeng, Zicheng Qi, Zekun Dou, Zhiyang Sontakke, Nitish Baek, Donghoon Ha, Sehoon Li, Tianyu Robotics Computer Vision and Pattern Recognition Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for evaluating these capabilities in real-world settings, and in direct comparison to humans, remain scarce. Existing evaluations often rely on pre-collected human motion datasets or simulation-based experiments, which limit reproducibility, overlook hardware factors, and hinder fair human-robot comparisons. We present Switch-JustDance, a low-cost and reproducible benchmarking pipeline that leverages motion-sensing console games, Just Dance on the Nintendo Switch, to evaluate robot whole-body control. Using Just Dance on the Nintendo Switch as a representative platform, Switch-JustDance converts in-game choreography into robot-executable motions through streaming, motion reconstruction, and motion retargeting modules and enables users to evaluate controller performance through the game's built-in scoring system. We first validate the evaluation properties of Just Dance, analyzing its reliability, validity, sensitivity, and potential sources of bias. Our results show that the platform provides consistent and interpretable performance measures, making it a suitable tool for benchmarking embodied AI. Building on this foundation, we benchmark three state-of-the-art humanoid whole-body controllers on hardware and provide insights into their relative strengths and limitations. |
| title | Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.17925 |