الباحثون

Xiao Hu

المنشورات 5

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World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models

Jiuyi Xu, Xiao Hu, Meida Chen وآخرون · 2026

Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures. Across four released checkpoints from two architecture families, we observe weak sensitivity to action changes and success-like predictions …

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Nudge Before You Push: Physics-Aware Navigation via Tactile Probing

Xianyao Li, Fang Xu, Ruitong Tian وآخرون · 2026

Visually identical containers can conceal loads that require different handling decisions. We present TANav, which uses a brief nudge to measure push resistance for navigation under a site-defined handling boundary. TacPhys reads the force sequence, with optional RGB-D and kinematics, into a mass estimate for push auth …

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KPI: A Promptable Kernel for Physical Interaction on Humanoids

Yikai Wang, Honghao Zhu, Xiao Hu وآخرون · 2026

Humanoids now walk, balance and reach with remarkable generality: one whole-body tracking policy follows references from a human, or from an end-to-end policy. That generality travels in the trajectory, and a trajectory alone carries limited information about the interaction it should produce: at contact, the executing …

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TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care

TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embeddings for downstream cardiac classification. It is designed to address the limitations of existing CLIP-style training, which often struggles with noisy clinical text …

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SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery

Jiajun Jiang, Chunliang Hua, Zichun Chen وآخرون · 2026

Urban uncrewed aerial vehicle (UAV) vision-language navigation (VLN) requires agents to follow instructions across extended urban spaces, inherently demanding long-term memory and geospatial grounding. However, scaling existing benchmarks remains difficult because of their reliance on costly reconstructed 3D assets, li …

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