This work introduces HumanTracker, a preference-aligned metric trained on 12K motion pairs containing 24K motions that better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.
Abstract
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contacts such as foot skating and mistimed touch-downs. Meanwhile, widely used test suites are small and lack the diversity needed to stress contact-rich, long-horizon behaviors. We introduce HumanTracker to make humanoid tracking evaluation both perceptually aligned and scalable. The HumanTracker benchmark contains approximately 153 hours of optical motion trajectories from multiple professional performers, organized into four motion families with text labels for fine-grained diagnosis. We further propose HumanScore, a preference-aligned metric trained on 12K motion pairs containing 24K motions. Across representative state-of-the-art trackers, HumanScore better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.
This work presents HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold, and introduces a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications.
Jiahao Ji, Ji Ma, Runhan Zhang et al.· 0 citations
An empirical study of common modeling and training factors used in recent humanoid motion-imitation pipelines, developing YAHMP, an open-source modular framework for training, evaluating, and deploying whole-body motion tracking policies on the Unitree G1.
Humanoid robots are increasingly expected to perform contact-rich tasks that require not only accurate whole-body motion but also robust physical interaction with surrounding objects and humans. Although recent advances in humanoid motion imitation and whole-body control have achieved remarkable tracking performance, existing datasets and benchmarks primarily focus on kinematic motion while largely overlooking synchronized interaction forces. As a result, current evaluations fail to capture how external interaction forces affect tracking accuracy, stability, and control robustness. In this paper, we present ThorArena, a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements. We collect a real-world interaction dataset that simultaneously captures whole-body human motion and forces exerted by both hands across six representative physical interaction tasks. Based on these demonstrations, we propose force-aware evaluation metrics that jointly assess whole-body tracking accuracy, robustness under different force levels, control effort, and episode survival through the Force-Aware Tracking Score (FATS) and complementary diagnostic metrics. We further establish a unified benchmark protocol that replays recorded interaction forces in simulation and provides a standardized evaluation interface for different humanoid control policies. Experiments on representative whole-body control policies demonstrate that force-aware evaluation reveals substantial performance differences that remain largely hidden under conventional no-force evaluation. ThorArena provides a practical and reproducible framework for studying force-aware humanoid interaction and offers a new benchmark for evaluating contact-rich humanoid behaviors.
Chenhao Yu, Hongwu Wang, Weitao Zhang et al.· 0 citations
StableMimic is presented, a unified tracker trained beyond the nominal tracking distribution that achieves the lowest errors on all four tracking metrics among five methods and attains the lowest values on six of seven post-fall motion and load measures, supporting improved interaction safety under this protocol.
Weihao Wu, Mingzhe Huang, Ruofei Liu et al.· 0 citations
GenTrack is introduced, an online generator--tracker framework that alternates execution-grounded, group-relative generator alignment with tracker training on newly generated references; anchoring and rehearsal constrain drift and demonstrates that joint online post-training effectively narrows the executability gap between retargeted references and robot-native motion.
Zeyu Ling, Xinyao Yu, Renye Yan et al.· 0 citations
RididBench is introduced, a simulator-grounded benchmark that compares a generated continuation with a reference rollout from the same initial frame and motion description, with per-frame masks, depth, 6-DoF trajectories, and contacts available for scoring.
Swarnim Jain, Shangzhe Wu· 2 citations
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