Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Abstract
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.
Cite
@article{arxiv.2511.17925,
title = {Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game},
author = {Jeonghwan Kim and Wontaek Kim and Yidan Lu and Jin Cheng and Fatemeh Zargarbashi and Zicheng Zeng and Zekun Qi and Zhiyang Dou and Nitish Sontakke and Donghoon Baek and Sehoon Ha and Tianyu Li},
journal= {arXiv preprint arXiv:2511.17925},
year = {2026}
}