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Weisheng Xu

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AAAI Conference 2026 Conference Paper

FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control

  • Jing Tan
  • Shiting Chen
  • Yangfan Li
  • Weisheng Xu
  • Renjing Xu

Unified physics-based humanoid controllers are pivotal for robotics and character animation, yet models that excel on gentle, everyday motions still stumble on explosive actions, hampering real-world deployment. We bridge this gap with FARM (Frame-Accelerated Augmentation and Residual Mixture-of-Experts), an end-to-end framework composed of frame-accelerated augmentation, a robust base controller, and a residual mixture-of-experts (MoE). Frame-accelerated augmentation exposes the model to high-velocity pose changes by widening inter-frame gaps. The base controller reliably tracks everyday low-dynamic motions, while the residual MoE adaptively allocates additional network capacity to handle challenging high-dynamic actions, significantly enhancing tracking accuracy. In the absence of a public benchmark, we curate the High-Dynamic Humanoid Motion (HDHM) dataset, comprising 3593 physically plausible clips. On HDHM, FARM reduces the tracking failure rate by 42.8% and lowers global mean per-joint position error by 14.6% relative to the baseline, while preserving near-perfect accuracy on low-dynamic motions. These results establish FARM as a new baseline for high-dynamic humanoid control and introduce the first open benchmark dedicated to this challenge.

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AAAI Conference 2023 Conference Paper

Enhanced Multi-Relationships Integration Graph Convolutional Network for Inferring Substitutable and Complementary Items

  • Huajie Chen
  • Jiyuan He
  • Weisheng Xu
  • Tao Feng
  • Ming Liu
  • Tianyu Song
  • Runfeng Yao
  • Yuanyuan Qiao

Understanding the relationships between items can improve the accuracy and interpretability of recommender systems. Among these relationships, the substitute and complement relationships attract the most attention in e-commerce platforms. The substitutable items are interchangeable and might be compared with each other before purchasing, while the complementary items are used in conjunction and are usually bought together with the query item. In this paper, we focus on two issues of inferring the substitutable and complementary items: 1) how to model their mutual influence to improve the performance of downstream tasks, 2) how to further discriminate them by considering the strength of relationship for different item pairs. We propose a novel multi-task learning framework named Enhanced Multi-Relationships Integration Graph Convolutional Network (EMRIGCN). We regard the relationship inference task as a link prediction task in heterogeneous graph with different types of edges between nodes (items). To model the mutual influence between substitute and complement, EMRIGCN adopts a two-level integration module, i.e., feature and structure integration, based on experts sharing mechanism during message passing. To obtain the strength of relationship for item pairs, we build an auxiliary loss function to further increase or decrease the distances between embeddings of items with weak or strong relation in latent space. Extensive experiments on both public and industrial datasets prove that EMRIGCN significantly outperforms the state-of-the-art solutions. We also conducted A/B tests on real world recommender systems of Meituan Maicai, an online supermarket platform in China, and obtained 15.3% improvement on VBR and 15.34% improvement on RPM.

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