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Linlin Ou

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3 papers

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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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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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