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Xinyi Yu

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks

Kaijun Wang
Liqin Lu
Mingyu Liu
Jianuo Jiang
Zeju Li
Bolin Zhang
Wancai Zheng
Xinyi Yu

Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have shown promise in enhancing spatial reasoning and task planning through learned semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges characteristic of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied in the literature. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination of locomotion and manipulation across challenging terrains. We further present the first comprehensive benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system’s generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks.

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EAAI Journal 2026 Journal Article

Robust classification method for printed circuit board defects based on virtual and real space cooperative diffusion model

Xinyi Yu
Haotian Sun
Yuanfu He
Jinmin Peng
Xianping Zeng
Liangshen Chen

Printed circuit board defect detection based on deep learning is a key technology in intelligent manufacturing. However, datasets collected in industrial environments often suffer from label noise due to inconsistent expert knowledge and ambiguous defect features, which severely degrades the performance of conventional models. To address this challenge, this paper proposes a robust classification framework based on a virtual-real space collaborative diffusion model. The proposed method introduces a distributed guidance mechanism to construct a latent semantic space (virtual space), which provides probabilistic priors for the inverse diffusion process in the real image space. Furthermore, a parameterized label encoding module is designed to mitigate the loss of fine-grained semantics during forward diffusion, and an attribute interaction attention mechanism is proposed to enhance the modeling of key defect attributes. Extensive experiments on the publicly available dataset with varying levels of label noise demonstrate that our method outperforms state-of-the-art approaches in terms of classification accuracy, robustness, and generated image quality, especially under strong noise conditions.

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ICRA Conference 2025 Conference Paper

A Novel Hybrid Hysteresis Modeling Method for Multiloop-Asymmetry Hysteresis Behavior of Nonlinear Compliant Actuators

Libo Zhou
Lingpeng Xu
Linlin Ou
Xinyi Yu
Yalei Feng
Shaoping Bai

Nonlinear compliant actuators are being increasingly used in human-robot interaction scenarios due to their inherent flexibility. However, a limitation is that nonlinear hysteresis exists, which will degrade the force/torque tracking performance if the hysteresis is not modeled accurately. Moreover, the existing methods are difficult to deal with the multi-loop asymmetry hysteresis. In this work, we present a novel modeling method, in which the hysteresis curves are decoupled into nonlinear reference lines and symmetrical hysteresis loops. A hybrid hysteresis model based on power function and Maxwellslip model is then developed to fit the nonlinear reference lines and symmetrical hysteresis loops respectively. Experiments were conducted on a nonlinear compliant actuator and the results show that the root-mean-square-errors (RMSE) of the hysteresis model decreases by 24. 4% when compared with the Maxwellslip based hysteresis model.

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AAAI Conference 2025 Conference Paper

Channel Merging: Preserving Specialization for Merged Experts

Mingyang Zhang
Jing Liu
Ganggui Ding
Linlin Ou
Xinyi Yu
Bohan Zhuang

Lately, the practice of utilizing task-specific fine-tuning has been implemented to improve the performance of large language models (LLM) in subsequent tasks. Through the integration of diverse LLMs, the overall competency of LLMs is significantly boosted. Nevertheless, traditional ensemble methods are notably memory-intensive, necessitating the simultaneous loading of all specialized models into GPU memory. To address the inefficiency, model merging strategies have emerged, merging all LLMs into one model to reduce the memory footprint during inference. Despite these advances, model merging often leads to parameter conflicts and performance decline as the number of experts increases. Previous methods to mitigate these conflicts include post-pruning and partial merging. However, both approaches have limitations, particularly in terms of performance and storage efficiency when merged experts increase. To address these challenges, we introduce Channel Merging, a novel strategy designed to minimize parameter conflicts while enhancing storage efficiency. This method initially clusters and merges channel parameters based on their similarity to form several groups offline. By ensuring that only highly similar parameters are merged within each group, it significantly reduces parameter conflicts. During inference, we can instantly look up the expert parameters from the merged groups, preserving specialized knowledge. Our experiments demonstrate that Channel Merging consistently delivers high performance, matching unmerged models in tasks like English and Chinese reasoning, mathematical reasoning, and code generation. Moreover, it obtains results comparable to model ensemble with just 53% parameters when used with a task-specific router.

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NeurIPS Conference 2024 Conference Paper

EfficientCAPER: An End-to-End Framework for Fast and Robust Category-Level Articulated Object Pose Estimation

Xinyi Yu
Haonan Jiang
Li Zhang
Lin Y. Wu
Linin Ou
Liu Liu

Human life is populated with articulated objects. Pose estimation for category-level articulated objects is a significant challenge due to their inherent complexity and diverse kinematic structures. Current methods for this task usually meet the problems of insufficient consideration of kinematic constraints, self-occlusion, and optimization requirements. In this paper, we propose EfficientCAPER, an end-to-end Category-level Articulated object Pose EstimatoR, eliminating the need for optimization functions as post-processing and utilizing the kinematic structure for joint-centric pose modeling, thus enhancing the efficiency and applicability. Given a partial point cloud as input, the EfficientCAPER firstly estimates the pose for the free part of an articulated object using decoupled rotation representation. Next, we canonicalize the input point cloud to estimate constrained parts' poses by predicting the joint parameters and states as replacements. Evaluations on three diverse datasets, ArtImage, ReArtMix, and RobotArm, show EfficientCAPER's effectiveness and generalization ability to real-world scenarios. The framework exhibits excellent static pose estimation performance for articulated objects, contributing to the advancement of category-level pose estimation. Codes will be made publicly available.

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ICRA Conference 2024 Conference Paper

Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints

Xinyi Yu
Liqin Lu
Jintao Rong
Guangkai Xu
Linlin Ou

3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent researches leverage the neural implicit surface as a global representation for 3D reconstruction. Equipped with data-driven pre-trained geometric cues, these methods have demonstrated promising performance. However, the inevitable inaccurate estimation of priors can lead to suboptimal reconstruction quality, particularly in some geometrically complex regions. In this paper, we propose a two-stage training process to further improve the reconstruction quality. It decouples the view-dependent and view-independent colors, and leverages two novel consistency constraints to enhance detail reconstruction performance without requiring extra priors. Additionally, we introduce an essential mask scheme to adaptively influence the selection of supervision constraints, thereby improving performance in a self-supervised paradigm. Experiments on synthetic and real-world datasets show the capability of reducing the side effects of inaccurately estimated priors and achieving high-quality scene reconstruction with rich geometric details.

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