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Chinese Control and Decision Conference, CCDC

作者： Chunhui He Qingxiang Wu Shuzhen Diao Ning Sun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In virtue of its excellent flexibility, high compliance, and convenient maintenance, pneumatic polishing equipment is widely used in the surface polishing of manufacturing products. However, due to the harsh working environment of polishing robots (e.g., high temperatures, humidity, and vibrations), the probability of inaccurate measurements is increased. At the same time, pneumatic systems have strong hysteresis and nonlinear characteristics because of the materials, friction, and gas resistance. The above issues will affect the accurate tracking performance of pneumatic control systems. Therefore, this paper proposes a force control framework considering hysteresis characteristics and sensor measurement sensitivity. Furthermore, an adaptive dynamic surface control method is designed to achieve precise force control under measurement sensitivity. In addition, the stability analysis is completed by using Lyapunov techniques, and all states of the closed-loop system are also guaranteed to be bounded. Finally, several groups of simulation results demonstrate the effectiveness of the proposed method.

关键词： Temperature measurement Sensitivity Force measurement Surface resistance Measurement uncertainty Pneumatic systems Robot sensing systems

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Sequential Manipulation Planning for Over-Actuated Unmanned Aerial Manipulators

Sequential Manipulation Planning for Over-Actuated Unmanned ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yao Su Jiarui Li Ziyuan Jiao Meng Wang Chi Chu Hang Li Yixin Zhu Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Automation Tsinghua University Institute for Artificial Intelligence Peking University

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

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Molecule Design by Latent Prompt Transformer 38

Molecule Design by Latent Prompt Transformer

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kong, Deqian Huang, Yuhao Xie, Jianwen Honig, Edouardo Xu, Ming Xue, Shuanghong Lin, Pei Zhou, Sanping Zhong, Sheng Zheng, Nanning Wu, Ying Nian Department of Statistics and Data Science UCLA United States Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China BioMap Research China Akool Research United States Institute of Engineering in Medicine UCSD United States Shu Chien-Gene Lay Department of Bioengineering UCSD United States

This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables. We propose the Latent Prompt Transformer (LPT), a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution modeled by a neural transformation of Gaussian white noise;(2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt;and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt. LPT can be learned by maximum likelihood estimation on molecule-property pairs. During property optimization, the latent prompt is inferred from target properties and constraints through posterior sampling and then used to guide the autoregressive molecule generation. After initial training on existing molecules and their properties, we adopt an online learning algorithm to progressively shift the model distribution towards regions that support desired target properties. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency. © 2024 Neural information processing systems foundation. All rights reserved.

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Surface Damage Detection for Bridge Cables Based on YOLOv7

Surface Damage Detection for Bridge Cables Based on YOLOv7

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2022 International Conference on Service robotics, ICoSR 2022

作者： Zhou, Zhixiang Luo, Yangzhixin Zhang, Xiaoguang Xiang, Renwei Li, Nan Chen, Huaping Ding, Ning School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (纸本)9781665454384

There is a growing demand for automatic bridge cable inspection using intelligent detection technologies to replace manual inspection. However, the application of intelligent inspection systems is hindered due to the lack of effective cable damage detection methods. To resolve this issue, this paper proposes a surface damage detection method for bridge cables based on YOLOv7, a powerful real-time object detector. We introduce the basic algorithm of YOLOv7, and elaborate its training, transfer, and deployment methods for cable damage detection. A dataset is built containing 7548 cable damage images collected from real bridge cables. The results show that this method can effectively detect common bridge cable defects, including scratches, dirt, and abrasions. The proposed method achieved an F1 score of 0.75 and an mAP score of 0.799 on the test set. The incorrect predictions are mainly caused by slight and vague defects that can be either labeled or not labeled. The inference efficiency of the proposed method reaches 20.5 FPS for images with resolution of 640×640 when deployed with NVIDIA Triton. Considering the diversity of bridge cables in real-world scenarios, we have also conducted model transfer analysis to discover how much additional data is needed to transfer a pre-trained model for a new bridge, which provides a valuable reference for applying transfer learning technology on bridge cable inspection. The proposed approach is practically useful for assisting humans in data processing, reducing labor input significantly, and improving the objectivity and consistency of cable inspection. © 2022 IEEE.

关键词： Damage detection

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Optimization-free Smooth Control Barrier Function for Polygonal Collision Avoidance

arXiv

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arXiv 2025年

作者： Wu, Shizhen Fang, Yongchun Sun, Ning Lu, Biao Liang, Xiao Zhao, Yiming Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Polygonal collision avoidance (PCA) is short for the problem of collision avoidance between two polygons (i.e., polytopes in planar) that own their dynamic equations. This problem suffers the inherent difficulty in dealing with non-smooth boundaries and recently optimization-defined metrics, such as signed distance field (SDF) and its variants, have been proposed as control barrier functions (CBFs) to tackle PCA problems. In contrast, we propose an optimization-free smooth CBF method in this paper, which is computationally efficient and proved to be nonconservative. It is achieved by three main steps: a lower bound of SDF is expressed as a nested Boolean logic composition first, then its smooth approximation is established by applying the latest log-sum-exp method, after which a specified CBF-based safety filter is proposed to address this class of problems. To illustrate its wide applications, the optimization-free smooth CBF method is extended to solve distributed collision avoidance of two underactuated nonholonomic vehicles and drive an underactuated container crane to avoid a moving obstacle respectively, for which numerical simulations are also performed. © 2025, CC BY.

关键词： Magnetic levitation vehicles

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STVO: Spatial-Temporal Constrained Velocity Obstacle for Safe Navigation among Pedestrians

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IEEE Transactions on Vehicular Technology 2025年

作者： Song, Yichen Wang, Runhua Bi, Qingchen Pan, Zhangchao Gao, Haiming Zhang, Xuebo Nankai University College of Artificial Intelligence Institute of Robotics and Automatic Information System The Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

This paper presents a novel motion planning method that safely navigates the unmanned ground vehicle (UGV) among pedestrian-rich environments by constructing the spatial-temporal constrained velocity obstacle (STVO) region. Compared with existing velocity obstacle (VO) type-based methods, the novelty of this work is twofold: 1) An innovative temporal constraint is introduced into the velocity space, also playing the role as an adaptive collision time constraint to dynamically adjust the velocity obstacle region according to real-time perceived pedestrian density;2) The spatial constraint is proposed to foresee and evade potential entrapments for the UGV, by carefully considering the position relationship between the UGV and the VO region. Additionally, a velocity resampling mechanism is further designed to prevent the UGV from being trapped. Above techniques augments the navigation flexibility and safety of the UGV, thereby significantly improves the success rate of practical navigation in dynamic and challenging pedestrian environments. Extensive simulations and real-world experiments have been conducted, especially the experiment in pedestrian-rich school cafeteria demonstrating satisfactory practicality of our approach. (Supplementary video link: https://***/xt4KosuPMYs). © 1967-2012 IEEE.

关键词： Pedestrian safety

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PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning

arXiv

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arXiv 2024年

作者： Guo, Haiyang Zhu, Fei Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences China

Existing federated learning methods have effectively dealt with decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each client dynamically learns new classes, requiring the global model to classify all seen classes. To effectively mitigate catastrophic forgetting and data heterogeneity under low communication costs, we propose a simple and effective method named PILoRA. On the one hand, we adopt prototype learning to learn better feature representations and leverage the heuristic information between prototypes and class features to design a prototype re-weight module to solve the classifier bias caused by data heterogeneity without retraining the classifier. On the other hand, we view incremental learning as the process of learning distinct task vectors and encoding them within different LoRA parameters. Accordingly, we propose Incremental LoRA to mitigate catastrophic forgetting. Experimental results on standard datasets indicate that our method outperforms the state-of-the-art approaches significantly. More importantly, our method exhibits strong robustness and superiority in different settings and degrees of data heterogeneity. The code is available at https://***/Ghy0501/PILoRA. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Backstepping-based adaptive control of indoor miniature airship

Backstepping-based adaptive control of indoor miniature airs...

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International Conference on Cybernetics, robotics and Control (CRC)

作者： Hongming Li Yongchun Fang Jinyang Dong Hai Yu Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Recently, research on the flight control of indoor small unmanned airships has attracted much attention. Unmanned airship has characteristics of low manufacturing cost, long dwell time, low energy consumption and so on. Under the premise of ignoring the external wind interference, a controller is designed that makes the airship move to the preset point and stay in the air. The mathematical model of the unmanned airship is first introduced in this paper. A six-degree-of-freedom equation of motion is established after force analysis, and then it is simplified to a three-degree-of-freedom model according to the geometric symmetry of the airship. Subsequently, the motion process of the airship is disassembled into horizontal motion and vertical motion, the backstepping method is used to design the controller, and unknown parameters in the model are processed with adaptive control. Finally, computer simulation experiments are carried out to verify the correctness of the model and the effectiveness of the controller from a mathematical point of view. In this paper, a small unmanned airship motion control system is designed, and its feasibility from the perspective of computer simulation is verified. These studies provide initial exploratory work on control methods for indoor unmanned airships.

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PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年 PP卷 PP页

作者： Zhu, Fei Zhang, Xu-Yao Cheng, Zhen Liu, Cheng-Lin Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics 999077 Hong Kong Institute of Automation of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China

Class-incremental learning (CIL) aims to continually recognize new classes while preserving the discriminability of previously learned ones. Most existing CIL methods are exemplar-based, relying on the storage and replay of a subset of old data during training. Without access to such data, these methods typically suffer from catastrophic forgetting. In this paper, we identify two fundamental causes of forgetting in CIL: representation bias and classifier bias. To address these challenges, we propose a simple yet effective dual-bias reduction framework, which leverages self-supervised transformation (SST) in the input space and prototype augmentation (protoAug) in the feature space. On one hand, SST mitigates representation bias by encouraging the model to learn generic, diverse representations that generalize across tasks. On the other hand, protoAug tackles classifier bias by explicitly or implicitly augmenting the prototypes of old classes in the feature space, thereby imposing stronger constraints to preserve decision boundaries. We further enhance the framework with hardness-aware prototype augmentation and multi-view ensemble strategies, yielding significant performance gains. The proposed framework can be easily integrated with pre-trained models. Without storing any samples of old classes, our method performs comparably to state-of-the-art exemplar-based approaches that rely on extensive data storage. We hope to draw the attention of researchers back to non-exemplar CIL by rethinking the necessity of storing old samples. © 1979-2012 IEEE.

关键词： Self-supervised learning

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