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Xin Fu

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3 papers

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EAAI Journal 2026 Journal Article

Adaptive kinematic modeling for soft continuum robots using Deep Belief Networks with an Event-Driven Incremental Learning strategy

Xin Fu
Daohui Zhang
Naijia Xu
Shuheng Ren
Yaqi Chu
Dezhen Xiong
Xingang Zhao

Precise kinematic modeling and control of soft continuum robots are challenged by their inherent deformability and complex interactions with the environment. This paper proposes an adaptive kinematic modeling framework based on a Deep Belief Network with Event-Driven Incremental Learning, designed for artificial intelligence-enabled control of soft robotic systems. The deep belief network is first pre-trained using simulated data to establish an initial kinematic model, requiring only approximately 600 real-world samples for deployment. An event-driven incremental learning mechanism is then introduced to adapt the model online. This mechanism is guided by a spike intensity metric, which evaluates prediction errors and selectively triggers either fine-tuning of network parameters or the integration of new radial basis function nodes to compensate for unmodeled kinematic effects and environmental disturbances. The adaptive kinematic model is embedded within a closed-loop controller to achieve accurate trajectory tracking. Experimental validation is conducted on a tendon-driven soft origami manipulator, covering trajectory tracking under varying payloads and deformation constraints, disturbance rejection, and teleoperation tasks. The proposed framework achieves sub-millimeter average trajectory tracking accuracy under confined conditions and outperforms baseline deep belief network models and piecewise constant curvature approaches. The results demonstrate that the proposed artificial intelligence-based adaptive kinematic modeling method provides a data-efficient and robust solution for precise control of soft continuum robots in applications such as medical robotics and flexible manufacturing.

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AAAI Conference 2025 Conference Paper

Achieving Lightweight Super-Resolution for Real-Time Computer Graphics

Yu Wen
Chen Zhang
Chenhao Xie
Xin Fu

Image super-resolution (SR) is essential for bridging the gap between modern hardware and real-time computer graphics (CG) applications. It reduces CG workload by allowing low-resolution rendering, with original quality restored later via mathematical operations or machine learning. However, recent learning-based SR methods often rely on complex models, demanding high computational resources and undermining the benefits of reduced rendering workload. Our qualitative and quantitative analysis of the SR process and rendering reveals that readily accessible rendering information can significantly enhance neural network design by serving as additional features. To capitalize on this, we propose CGSR, an optimization framework designed for lightweight real-time super-resolution. CGSR utilizes rendering information to boost both network extensibility and efficiency. It utilizes progressively available rendering information from the pipeline, which arrives earlier than the rendered frame, enabling pre-processing and masking of latency. These features are then integrated into a selected SR network backbone to form a CG-enhanced network. This network is further optimized and refined into a CG-optimized version using neural architecture search (NAS). To improve runtime performance, CGSR also employs rendering-aware hybrid pruning, which dynamically prunes the network based on temporal rendering data. Evaluation results show that CGSR significantly reduces parameter size, multi-add operations, and inference time while maintaining high SR quality across various backbone SR networks.

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AAAI Conference 2025 Conference Paper

WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling

Huai-An Su
Jiaxiang Geng
Liang Li
Xiaoqi Qin
Yanzhao Hou
Hao Wang
Xin Fu
Miao Pan

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

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