ThorArena is presented, a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements and a unified benchmark protocol that replays recorded interaction forces in simulation and provides a standardized evaluation interface for different humanoid control policies.
Abstract
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.
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.
Dai-En Liu, Ze-Kun Qi, Jia-Yu Zeng et al.· 0 citations
Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object motion. Human demonstrations provide examples of coordinated interaction, but transferring these behaviors to humanoid robots requires learning how to establish and main...
Liu Cao, Xing-Ze Wu, Jing-Zhi Cui et al.· 0 citations
Whole-body humanoid teleoperation commonly combines a motion-tracking policy with a separate dexterous-hand retargeter. However, independently generated commands do not explicitly preserve body-hand geometric relations, leading to mismatches in relative wrist poses and fingertip positions during bimanual interaction. W...
Rui Wu, Shuang Li, Li-Ding Zhang et al.· 1 citation
Safe human-to-humanoid motion imitation is crucial for shared environments, where direct motion retargeting may induce self-collision or human–humanoid collision due to embodiment mismatch, kinematic limits, perception uncertainty, and human proximity. This paper presents an online vision-aided safe human-to-humanoid m...
Wenqi Cai, John Abanes, N. Evangeliou et al.· Big Data and Cognitive Compu...· 0 citations
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
General-purpose motion trackers enable humanoid robots to follow diverse whole-body motions while maintaining balance, but are trained only on flat ground, failing to exploit bipedal mobility over complex terrain. Cross-terrain controllers, meanwhile, are task-specific or accept only low-dimensional locomotion commands...
Zi-Yang Cheng, Tian-Shu Tang, Jin-Xi Lan et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.