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Industry researchasia · cn
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Research library38linked papers
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Selected work

Representative Papers

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Aug 13, 2026

Existing evaluations of humanoid motion tracking rely primarily on kinematic errors, which fail to capture physically implausible distortions perceptible to humans—such as foot sliding or incorrect contact—and suffer from small-scale, low-diversity test sets. To address these limitations, this work introduces HumanTracker, a large-scale and diverse benchmark comprising 153 hours of optical motion capture data from professional actors, covering four action categories with accompanying textual annotations. Furthermore, the authors propose HumanScore, a human-aligned evaluation metric derived from a preference model trained on 12K motion pairs (24K individual motions). HumanScore enables fine-grained diagnosis of critical physical properties like contact fidelity and support stability, significantly outperforming conventional metrics across multiple state-of-the-art trackers, accurately predicting human preferences, and uncovering previously overlooked physical inconsistencies.

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Scaling Behavior Foundation Model for Humanoid Robots

Jul 16, 2026

Humanoid robot control faces significant challenges in whole-body coordination, real-time responsiveness, and cross-scenario generalization, with existing approaches limited in scalability and universality. This work proposes a scalable behavioral foundation model that achieves breakthrough performance through three core innovations: a global-frame-based motion tracking learning paradigm, a co-designed strategy involving the number of policy rollouts and diversity of reference motions, and a novel Humanoid Transformer architecture. The study systematically uncovers a scalable pathway for behavioral foundation models, enabling structured behavioral representations to emerge naturally. Evaluated in both simulation and real-world deployment, the approach substantially improves performance, reducing MPKPE by over 10% on local motion patterns and by 82% on global motion patterns in the test set.

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Recent publications

Latest Papers

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Aug 13, 2026

Existing evaluations of humanoid motion tracking rely primarily on kinematic errors, which fail to capture physically implausible distortions perceptible to humans—such as foot sliding or incorrect contact—and suffer from small-scale, low-diversity test sets. To address these limitations, this work introduces HumanTracker, a large-scale and diverse benchmark comprising 153 hours of optical motion capture data from professional actors, covering four action categories with accompanying textual annotations. Furthermore, the authors propose HumanScore, a human-aligned evaluation metric derived from a preference model trained on 12K motion pairs (24K individual motions). HumanScore enables fine-grained diagnosis of critical physical properties like contact fidelity and support stability, significantly outperforming conventional metrics across multiple state-of-the-art trackers, accurately predicting human preferences, and uncovering previously overlooked physical inconsistencies.

0 citationsRead paper

Scaling Behavior Foundation Model for Humanoid Robots

Jul 16, 2026

Humanoid robot control faces significant challenges in whole-body coordination, real-time responsiveness, and cross-scenario generalization, with existing approaches limited in scalability and universality. This work proposes a scalable behavioral foundation model that achieves breakthrough performance through three core innovations: a global-frame-based motion tracking learning paradigm, a co-designed strategy involving the number of policy rollouts and diversity of reference motions, and a novel Humanoid Transformer architecture. The study systematically uncovers a scalable pathway for behavioral foundation models, enabling structured behavioral representations to emerge naturally. Evaluated in both simulation and real-world deployment, the approach substantially improves performance, reducing MPKPE by over 10% on local motion patterns and by 82% on global motion patterns in the test set.

0 citationsRead paper