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Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method

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arXiv 2025年

作者： Dong, Jinyang Wu, Shizhen Liu, Rui Liang, Xiao Lu, Biao Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300071 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear disturbance observer (DOB) based on the robust integral of the sign of the error (RISE) is used to estimate system uncertainties, ensuring that the estimation error converges to zero exponentially. This error bound is integrated into the safety-critical controller to reduce conservativeness while ensuring safety. To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem. To ensure that the feasible space does not vanish under disturbances, a DOB-VCBF-based method is introduced, ensuring system safety while maintaining the feasibility of the resulting QP. Subsequently, several groups of simulation and experimental results are provided to validate the effectiveness of the proposed controller. © 2025, CC BY.

关键词： Quadratic programming

Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing

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IEEE Transactions on Mobile Computing 2025年

作者： Ye, Wentao Luo, Yuan Liu, Bo Huang, Jianwei The Chinese University of Hon g Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering Shenzhen Guangdong518172 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Shenzhen Guangdong518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong518172 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen Guangdong518172 China

The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and heterogeneity, leading to either compromised map freshness or excessive recruitment costs. Furthermore, these characteristics complicate the characterization of the feasible space of the optimal recruitment policy, necessitating a method to compute it efficiently in dynamic transportation scenarios. To overcome these challenges, we propose an efficient and cost-effective vehicle recruitment (ENTER) mechanism. Specifically, the ENTER mechanism has a threshold structure and balances freshness with recruitment costs while accounting for the vehicles' random arrival and heterogeneity. It also integrates the bound-based relative value iteration (RVI) algorithm, which utilizes the threshold-type structure and upper bounds of thresholds to reduce the feasible space and expedite convergence. Numerical results show that the proposed ENTER mechanism increases the HD map company's payoff by 23.40% and 43.91% compared to state-of-the-art mechanisms that do not account for vehicle heterogeneity and random arrivals, respectively. Furthermore, the bound-based RVI algorithm in the ENTER mechanism reduces computation time by an average of 18.91% compared to the leading RVI-based algorithm. © 2025 IEEE.

关键词： Markov processes

Playing Non-Embedded Card-Based Games with Reinforcement Learning

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arXiv 2025年

作者： Wu, Tianyang Wan, Lipeng Wang, Yuhang Wan, Qiang Lan, Xuguang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University No. 28 West Xianning Road Xi’an China

Significant progress has been made in AI for games, including board games, MOBA, and RTS games. However, complex agents are typically developed in an embedded manner, directly accessing game state information, unlike human players who rely on noisy visual data, leading to unfair competition. Developing complex non-embedded agents remains challenging, especially in card-based RTS games with complex features and large state spaces. We propose a non-embedded offline reinforcement learning training strategy using visual inputs to achieve real-time autonomous gameplay in the RTS game Clash Royale1. Due to the lack of a object detection dataset for this game, we designed an efficient generative object detection dataset for training. We extract features using state-of-the-art object detection and optical character recognition models. Our method enables real-time image acquisition, perception feature fusion, decision-making, and control on mobile devices, successfully defeating built-in AI opponents. All code is open-sourced at https://***/wty-yy/katacr. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Exploring the Developmental Aspects of the Uncanny Valley Effect on Children’s Preferences for Robot Appearance

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International Journal of Human-Computer Interaction 2025年第10期41卷 6366-6376页

作者： Chien, Sung-En Chen, Yu-Shan Chen, Yi-Chuan Yeh, Su-Ling Department of Psychology National Taiwan University Taiwan Ganzin Technology Inc Taiwan Department of Medicine Mackay Medical College Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taiwan National Humanities Center Research Triangle Park NC United States

As robotic technology advances, robots are increasingly being designed and utilized for applications involving children across various fields. Since the acceptance of robots is profoundly affected by their appearance, understanding children’s preferences for the looks of robots becomes imperative. The uncanny valley effect (UVE) posits that the perceived likeability of robots increases as their appearance becomes more humanlike but then sharply drops when they approach near-perfect human similarity before rising once an extremely realistic likeness is achieved. Due to inconsistent results in the literature regarding when humans develop a sense of uncanniness toward humanlike robots, the present study aims to examine the emergence of the UVE in children. 67 1st-graders (aged six to seven years) and 95 4th-graders (aged nine to ten years) evaluated 12 robot faces on the scales of likeability, disgust, humanness, and intention to play with the robots (i.e., acceptance). The results demonstrated that the UVE was not observed in 1st-graders or 4th-grade boys. Nevertheless, a UVE-like pattern emerged among 4th-grade girls, particularly in their ratings of likeability and acceptance as a function of robots’ degree of humanness. A positive correlation between likeability and acceptance was consistently found across all age and gender groups. Additionally, negative correlations were found between likeability and disgust, as well as disgust and acceptance, specifically in the fourth graders but not in the first graders. These results suggest that the UVE may not be fully developed in 10-year-old children, with girls potentially exhibiting earlier development than boys. We conclude that age and gender differences should be considered when evaluating children’s preferences for robot appearance and their willingness to interact with robots. © 2024 Taylor & Francis Group, LLC.

关键词： Human robot interaction

CPAL: Cross-prompting Adapter with LoRAs for RGB+X Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Ye Wu, Pengfei Wang, Miaohui Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence Nanjing China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications Lancaster United Kingdom

As sensor technology evolves, RGB+X systems combine traditional RGB cameras with another type of auxiliary sensor, which enhances perception capabilities and provides richer information for important tasks such as semantic segmentation. However, acquiring massive RGB+X data is difficult due to the need for specific acquisition equipment. Therefore, traditional RGB+X segmentation methods often perform pretraining on relatively abundant RGB data. However, these methods lack corresponding mechanisms to fully exploit the pretrained model, and the scope of the pretraining RGB dataset remains limited. Recent works have employed prompt learning to tap into the potential of pretrained foundation models, but these methods adopt a unidirectional prompting approach i.e., using X or RGB+X modality to prompt pretrained foundation models in RGB modality, neglecting the potential in non-RGB modalities. In this paper, we are dedicated to developing the potential of pretrained foundation models in both RGB and non-RGB modalities simultaneously, which is non-trivial due to the semantic gap between modalities. Specifically, we present the CPAL (Cross-prompting Adapter with LoRAs), a framework that features a novel bi-directional adapter to simultaneously fully exploit the complementarity and bridging the semantic gap between modalities. Additionally, CPAL introduces low-rank adaption (LoRA) to fine-tune the foundation model of each modal. With the support of these elements, we have successfully unleashed the potential of RGB foundation models in both RGB and non-RGB modalities simultaneously. Our method achieves state-of-the-art (SOTA) performance on five multi-modal benchmarks, including RGB+Depth, RGB+Thermal, RGB+Event, and a multi-modal video object segmentation benchmark, as well as four multi-modal salient object detection benchmarks. The code and results are available at: https://***/abelny56/CPAL. © 1991-2012 IEEE.

关键词： Semantic Segmentation

Development of Assistance System for Online Dispute Resolution and Legal Advice Using AIML

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Development of Assistance System for Online Dispute Resoluti...

Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Neelesh Kumar Sahu Rishabh Soni Priyanshu Namdeo Nikhil Mishra P Humanshu Naidu Priyansh Pawar Artificial Intelligence and Robotics Gyan Ganga Institute of Technology and Science Jabalpur India Computer Science and Engineering Gyan Ganga Institute of Technology and Science Jabalpur India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In the present work, the main goal is to reduce the downsides of litigation and other traditional conflict resolution procedures. Many people and corporations avoid litigation due to its high costs, extended timelines, and mental stress. This methodology presents a fresh perspective on conflict resolution, one that makes use of technology to simplify the process and provides an affordable and easily accessible option. At the heart of this strategy is an Online Dispute Resolution (ODR) platform that allows for the digital resolution of disputes, doing away with the necessity for physical presence and the expenses that come along with it. Everyone from sole proprietors to large corporations, regardless of their level of legal knowledge, may utilize the platform because to its intuitive design. Eventually, this initiative seeks to enable individuals and companies to confidently and easily manage legal difficulties by reshaping the landscape of legal dispute resolution with an emphasis on speed, accessibility, and openness. Reduced legal fees, reduced time to resolution, and less emotional toll on parties are just a few of the many advantages of this strategy. People with private legal issues and small and medium-sized businesses (SMEs) resolving business conflicts are among our project's intended users.

关键词： Heart Costs Law Navigation Human factors Companies Information and communication technology artificial intelligence Business

Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models

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arXiv 2025年

作者： Lv, Xingtai Sun, Youbang Zhang, Kaiyan Qu, Shang Zhu, Xuekai Fan, Yuchen Wu, Yi Hua, Ermo Long, Xinwei Ding, Ning Zhou, Bowen Department of Electronic Engineering Tsinghua University China Shanghai Artificial Intelligence Laboratory China Robotics Institute Carnegie Mellon University United States

State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs. Copyright © 2025, The Authors. All rights reserved.

关键词： State space methods

Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation

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arXiv 2025年

作者： Liao, Bencheng Tao, Hongyuan Zhang, Qian Cheng, Tianheng Li, Yingyue Yin, Haoran Liu, Wenyu Wang, Xinggang Institute of Artificial Intelligence Huazhong University of Science & Technology China School of EIC Huazhong University of Science & Technology China Horizon Robotics China

Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native multimodal state space models through progressive distillation from existing MLLMs using moderate academic computational resources. Our approach enables the direct conversion of trained decoder-only MLLMs to linear-complexity architectures without requiring pre-trained RNN-based LLM or vision encoders. We propose an seeding strategy to carve Mamba from trained Transformer and a three-stage distillation recipe, which can effectively transfer the knowledge from Transformer to Mamba while preserving multimodal capabilities. Our method also supports flexible hybrid architectures that combine Transformer and Mamba layers for customizable efficiency-performance trade-offs. Distilled from the Transformer-based decoder-only HoVLE, mmMamba-linear achieves competitive performance against existing linear and quadratic-complexity VLMs, while mmMamba-hybrid further improves performance significantly, approaching HoVLE’s capabilities. At 103K tokens, mmMamba-linear demonstrates 20.6× speedup and 75.8% GPU memory reduction compared to HoVLE, while mmMamba-hybrid achieves 13.5× speedup and 60.2% memory savings. Code and models are released at https: //***/hustvl/mmMamba © 2025, CC BY-NC-SA.

关键词： Decoding

OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models

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arXiv 2025年

作者： Zou, Jialv Liao, Bencheng Zhang, Qian Liu, Wenyu Wang, Xinggang School of EIC Huazhong University of Science & Technology China Institute of Artificial Intelligence Huazhong University of Science & Technology China Horizon Robotics China

Recent advancements in unified multimodal understanding and visual generation (or multimodal generation) models have been hindered by their quadratic computational complexity and dependence on large-scale training data. We present OmniMamba, the first linear-architecture-based multimodal generation model that generates both text and images through a unified next-token prediction paradigm. The model fully leverages Mamba-2’s high computational and memory efficiency, extending its capabilities from text generation to multimodal generation. To address the data inefficiency of existing unified models, we propose two key innovations: (1) decoupled vocabularies to guide modality-specific generation, and (2) task-specific LoRA for parameter-efficient adaptation. Furthermore, we introduce a decoupled two-stage training strategy to mitigate data imbalance between two tasks. Equipped with these techniques, OmniMamba achieves competitive performance with JanusFlow while surpassing Show-o across benchmarks, despite being trained on merely 2M image-text pairs, which is 1,000 times fewer than Show-o. Notably, OmniMamba stands out with outstanding inference efficiency, achieving up to a 119.2× speedup and 63% GPU memory reduction for long-sequence generation compared to Transformer-based counterparts. Code and models are released at https://***/hustvl/ OmniMamba Copyright © 2025, The Authors. All rights reserved.

关键词： State space methods