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Xinxing Chen

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4 papers

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AAAI Conference 2026 Conference Paper

Direction Sensitivity–Based Knowledge Distillation: Optimization-Aware Low-Rank Knowledge Transfer

Yongkai Liao
Xinxing Chen
Zhongzheng Fu
Haoyuan Wang
Jian Huang

Knowledge distillation (KD) aims to enhance the performance of lightweight student networks through the guidance of teacher models. However, the existing methods have deficiencies in two key aspects: First, these methods rely heavily on static representation alignment, failing to account for optimization sensitivity in different directions within the distillation subspace; second, they lack a fine-grained mechanism to align critical directional features. To address these issues, we propose Direction Sensitivity–based Knowledge Distillation method (DSKD), which can quantitatively measure the sensitivity of each direction to the loss function at different training stages and dynamically select the optimization direction accordingly. Meanwhile, we designed a directional sensitivities weighted distillation loss. By aligning the parameter matrices of the teacher and student models in the key directions, we can more effectively transfer knowledge and improve the distillation effect. We combined DSKD with multiple advanced distillation strategies and conducted an empirical evaluation in the GLUE benchmark and CIFAR-100. The results showed that this method could significantly improve the performance of existing distillation techniques.

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IROS Conference 2025 Conference Paper

Safe Corridor-Based MPC for Follow-Ahead and Obstacle Avoidance of Mobile Robot in Cluttered Environments

Yikun Zhang
Xinxing Chen
Jian Huang 0001

In cluttered environments, a human-following mobile robot must predict the motion intention of the followed human and take environmental obstacles into consideration. Consequently, it brings several challenges, such as the human’s detour direction prediction problem and the visibility maintenance problem for route planning. To overcome these problems, this paper proposes an integrated follow-ahead framework, in which the human’s detour behavior is predicted by the Leg Motion Model-based EKF (LMM-EKF) and the iterative human route search algorithm, followed by the Safe Corridor-based Model Predictive Controller (SCMPC) used to obtain the optimal control solution. Also, a new perspective about visibility is provided in this paper that, via placing multiple obstacle-free safe regions along the human’s intended direction without any complex preprocessing for the point cloud, SCMPC prevents the robot from collision and occlusion simultaneously based on the basic properties of the convex set. The validity of the proposed method is comprehensively verified through real-world experiments.

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IROS Conference 2024 Conference Paper

Enhancing Prosthetic Safety and Environmental Adaptability: A Visual-Inertial Prosthesis Motion Estimation Approach on Uneven Terrains

Chuheng Chen
Xinxing Chen
Shucong Yin
Yuxuan Wang 0006
Binxin Huang
Yuquan Leng
Chenglong Fu 0001

Environment awareness is crucial for enhancing walking safety and stability of amputee wearing powered prosthesis when crossing uneven terrains such as stairs and obstacles. However, existing environmental perception systems for prosthesis only provide terrain types and corresponding parameters, which fail to prevent potential collisions when crossing uneven terrains and may lead to falls and other severe consequences. In this paper, a visual-inertial motion estimation approach is proposed for prosthesis to perceive its movement and the changes of spatial relationship between the prosthesis and uneven terrain when traversing them. To achieve this, we estimate the knee motion by utilizing a depth camera to perceive the environment and align feature points extracted from uneven terrains. Subsequently, an error-state Kalman filter is incorporated to fuse the inertial data into visual estimations to obtain a more robust and accurate estimation, which is then utilized to derive the motion of the whole prosthesis for our prosthetic control scheme. Experiments conducted on our collected dataset and stair walking trials with powered prosthesis show that the proposed method can accurately track the motion of human leg and the prosthesis with the average root-mean-square error of toe trajectory less than 5 cm. The proposed method is expected to enable the environmental adaptive control for prosthesis, thereby enhancing amputee’s safety and mobility in uneven terrains.

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JBHI Journal 2023 Journal Article

Natural Grasp Intention Recognition Based on Gaze in Human–Robot Interaction

Bo Yang
Jian Huang
Xinxing Chen
Xiaolong Li
Yasuhisa Hasegawa

Objective: While neuroscience research has established a link between vision and intention, studies on gaze data features for intention recognition are absent. The majority of existing gaze-based intention recognition approaches are based on deliberate long-term fixation and suffer from insufficient accuracy. In order to address the lack of features and insufficient accuracy in previous studies, the primary objective of this study is to suppress noise from human gaze data and extract useful features for recognizing grasp intention. Methods: We conduct gaze movement evaluation experiments to investigate the characteristics of gaze motion. The target-attracted gaze movement model (TAGMM) is proposed as a quantitative description of gaze movement based on the findings. A Kalman filter (KF) is used to reduce the noise in the gaze data based on TAGMM. We conduct gaze-based natural grasp intention recognition evaluation experiments to collect the subject's gaze data. Four types of features describing gaze point dispersion ( $f_{var}$ ), gaze point movement ( $f_{gm}$ ), head movement ( $f_{hm}$ ), and distance from the gaze points to objects ( $f_{d_{j}}$ ) are then proposed to recognize the subject's grasp intentions. With the proposed features, we perform intention recognition experiments, employing various classifiers, and the results are compared with different methods. Results: The statistical analysis reveals that the proposed features differ significantly across intentions, offering the possibility of employing these features to recognize grasp intentions. We demonstrated the intention recognition performance utilizing the TAGMM and the proposed features in within-subject and cross-subject experiments. The results indicate that the proposed method can recognize the intention with accuracy improvements of 44. 26% (within-subject) and 30. 67% (cross-subject) over the fixation-based method. The proposed method also consumes less time (34. 87 ms) to recognize the intention than the fixation-based method (about 1 s). Conclusion: This work introduces a novel TAGMM for modeling gaze movement and a variety of practical features for recognizing grasp intentions. Experiments confirm the effectiveness of our approach. Significance: The proposed TAGMM is capable of modeling gaze movements and can be utilized to process gaze data, and the proposed features can reveal the user's intentions. These results contribute to the development of gaze-based human-robot interaction.

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