Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game

TL;DR

This paper introduces Switch-JustDance, a low-cost, reproducible benchmarking method using Nintendo Switch's Just Dance game to evaluate humanoid robot controllers in real-world settings.

Contribution

It presents a novel benchmarking pipeline that converts game choreography into robot motions, enabling fair comparison of control algorithms on actual hardware.

Findings

The platform provides consistent and interpretable performance measures.

Benchmarking three state-of-the-art controllers reveals their relative strengths and limitations.

Validation shows the method's reliability, validity, and sensitivity.

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.