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检索条件"丛书名=Lecture notes in artificial intelligence,"

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A LLM-Based Robot Partner with Multi-modal Emotion Recognition 17th

A LLM-Based Robot Partner with Multi-modal Emotion Recogniti...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Jiang, Yutong Shao, Shuai Dai, Yaping Hirota, Kaoru Beijing Inst Technol Beijing 100081 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

The integration of Large Language Models (LLMs) with robotic systems has opened new avenues for the development of empathetic and interactive robot partners. This paper introduces a service robot system that incorporates multi-modal emotion recognition and LLM-based emotion dialogue generation. The system captures user emotions through a tri-modal emotion recognition model (TriMER), which processes audio, text, and facial expressions using advanced techniques like BiLSTM, CNN, and Deformable Convolutional Networks (DCN). Experiments conducted using the IEMOCAP dataset show that our TriMER model achieves an accuracy of 74.15% in recognizing emotions. By combining emotion recognition with LLM, the robot can better understand and respond to human emotions, facilitating more natural and empathetic interactions. This development holds promise for applications in elder care, aiming to enhance both physical and mental well-being.

关键词： Robot Partners Multi-modal Emotion Recognition Large Language Models Human-Robot Interaction

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Identification of Wiener-Hammerstein Model Using Stochastic Variational Bayesian Learning 17th

Identification of Wiener-Hammerstein Model Using Stochastic ...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Li, Junhao Zhang, Fukai Wang, Cong Liu, Yuehu Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

The complexity of probability estimation has limited the application of Bayesian learning in nonlinear system identification. This paper addresses Wiener-Hammerstein (WH) nonlinear process identification in the presence of process noise and measurement noise, we propose a Stochastic Variational Inference (SVI) method inspired by stochastic optimization. The SVI method leverages probabilities of intermediate variables to estimate natural gradients of model parameters and updates the posterior probabilities of hidden variables. Compared to the traditional Variational Inference (VI) method, our proposed approach significantly reduces computational complexity. The effectiveness of the SVI method is verified by two numerical simulations and the WH benchmark problem, thereby providing a fresh perspective for efficiently identifying nonlinear systems with large-scale uncertain data.

关键词： Bayesian learning Stchastic optimization nonlinear system identification Wiener-Hammerstein model

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A Soft Amphibious Robot with Buoyancy Control and Underwater Manipulation Capabilities 17th

A Soft Amphibious Robot with Buoyancy Control and Underwater...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Yang, Yang Zheng, Haozhe Xie, Yuan Jiang, Pei Li, Yingtian Nanjing Univ Informat Sci & Technol NUIST Sch Automat Nanjing 210044 Peoples R China NUIST Jiangsu Prov Engn Res Ctr Intelligent Meteorol Ex Nanjing 210044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9789819607945;9789819607952

Underwater equipment is critical for environmental applications. Conventional rigid underwater manipulators require considerable size and weight, hindering the application of underwater operations. Origami actuators have proven to be an effective technique and have been used in many applications. In this work, I propose a bionic soft amphibious robot based on a Z-shaped actuator and a twisted tower actuator. The soft robot can be fabricated by 3D printing technology and has a simple structure for easy operation. Two different types of programmable origami actuators are designed and fabricated, i.e., Z-shaped actuator and torsion tower actuator. Z-shaped actuator is used for the rear leg which enables the movement of the frog. Meanwhile, the torsion tower shaped actuator is used for the front legs to rotate the joints and movement on land. We designed a novel hybrid structure (rigid frame + soft actuator) gripper using the Z-shaped actuator to improve the gripping performance. And we use bellows to make the buoyancy unit of the soft robot. The origami actuators and were tested through a series of experiments, which showed that the robot was able to efficiently move and perform grasping maneuvers in water and on land. Our results demonstrate the effectiveness of these actuators in generating the desired motions and provide insight into the potential of applying 3D printed origami actuators to develop soft robots with bionic capabilities.

关键词： Soft Body Robots Origami Actuators Underwater Manipulation Bionic Robots Amphibious Locomotion

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Fixed-Time Fault-Tolerant Control for Robotic Manipulators Based on Bias Neural Network with Input Saturation 17th

Fixed-Time Fault-Tolerant Control for Robotic Manipulators B...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Guo, Zihang Xue, Shuangsi Li, Huan Tan, Junkai Zheng, Xiaodong Xia, Liangliang Cao, Hui Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

In this article, a fixed-time fault-tolerant trajectory tracking control based on bias neural network is proposed for robotic manipulators with input saturation and actuator faults. Firstly, a dynamic model of the multi-joint robotic manipulators is developed, incorporating input saturation and actuator faults. Subsequently, a method to compensate for input saturation is devised, aimed at achieving control input compensation within a fixed-time frame. Following that, a fixed-time fault-tolerant control strategy is introduced, utilizing a bias neural network to approximate disturbances and the total faults of the actuators. Finally, The Lyapunov theory is employed to demonstrate the global fixed-time convergence of the system. Simulation results are conducted to validate the robustness and rapid convergence of the proposed control method within a specified time frame.

关键词： Robotic manipulators Fault-tolerant control Input saturation Fix-time Bias neural network

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Towards Mass Spectrum Analysis with ASP 17th

Towards Mass Spectrum Analysis with ASP

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17th International Conference on Logic Programming and Non-monotonic Reasoning

作者： Kuechenmeister, Nils Vliev, Alex Kroetzsch, Markus Tech Univ Dresden Knowledge Based Syst Grp Dresden Germany

ISBN: (纸本)9783031742088;9783031742095

We present a new use of Answer Set Programming (ASP) to discover the molecular structure of chemical samples based on the relative abundance of elements and structural fragments, as measured in mass spectrometry. To constrain the exponential search space for this combinatorial problem, we develop canonical representations of molecular structures and an ASP implementation that uses these definitions. We evaluate the correctness of our implementation over a large set of known molecular structures, and we compare its quality and performance to other ASP symmetry-breaking methods and to a commercial tool from analytical chemistry.

关键词： ASP symmetry breaking molecular structure chemistry

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Approximate Compression of CNF Concepts 27th

Approximate Compression of CNF Concepts

引用

27th International Conference on Discovery Science

作者： Bocklandt, Sieben Derkinderen, Vincent Kimmig, Angelika De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium KU Leuven Inst AI Leuven AI Leuven Belgium Orebro Univ Ctr Appl Autonomous Syst Orebro Sweden

ISBN: (纸本)9783031789793;9783031789809

We consider a novel concept-learning and merging task, motivated by two use-cases. The first is about merging and compressing music playlists, and the second about federated learning with data privacy constraints. Both settings involve multiple learned concepts that must be merged and compressed into a single interpretable and accurate concept description. Our concept descriptions are logical formulae in CNF, for which merging, i.e. disjoining, multiple CNFs may lead to very large concept descriptions. To make the concepts interpretable, we compress them relative to a dataset. We propose a new method named CoWC (Compression Of Weighted Cnf) that approximates a CNF by exploiting techniques of itemset mining and inverse resolution. CoWC compresses the CNF size while also considering the F1-score w.r.t. the dataset. Our empirical evaluation shows that CoWC outperforms alternative compression approaches.

关键词： Concept learning Formula compression

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Multi-contact Trajectory Planning of Humanoid Based on Relaxed Contact Constraint 17th

Multi-contact Trajectory Planning of Humanoid Based on Relax...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Liu, Jiongnan Gao, Junyao Wu, Taiping Zuo, Weilong Xin, Xilong Cao, Jingwei Beijing Inst Technol Sch Mechatron Engn Beijing 10081 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

Humanoid robots are capable of imitating most human actions due to their joint configuration being similar to that of human. Research on motion planning for humanoid robots often focuses on the legs, aiming to plan legs' trajectories to achieve movements such as walking, running, and jumping. However, when humanoid robots operate in real human environments, relying solely on foot-end contact is often insufficient due to the complexity of the environment. Multi-contact trajectory planning greatly expands the working space of humanoid robots, enabling them to confidently navigate complex environments. To plan multi-contact and whole-body motions for humanoids, we explore a trajectory optimization framework and incorporate relaxed contact constraints to ensure that the solver can find feasible solutions. We introduce a full-body dynamic model for humanoid robots and simplified it into a multi-link model. During the planning process, collision points for the robot are pre-specified, and the kinematics and dynamics of these collision points are derived. The relaxed contact constraints reconcile the conflict between contact forces and contact distances while ensuring the continuity of contact dynamics. We demonstrate that this algorithm is capable of generating multi-contact motion plans with a humanoid robot. In real experiments, humanoid robot BHR-FCR achieve multi-contact motion on flat terrain.

关键词： Trajectory optimization Relaxed contact constraint Humanoid robot

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Enhancing Music Genre Classification Using Augmented Features Ensemble Learning Technique 21st

Enhancing Music Genre Classification Using Augmented Feature...

引用

21st Pacific Rim International Conference on artificial intelligence

作者： Shariat, Raad Zhang, John Z. Univ Lethbridge Dept Math & Comp Sci Lethbridge AB T1K 3M2 Canada

ISBN: (纸本)9789819601158;9789819601165

In Music Information Retrieval, classification of music genres is a core task and has been gaining increasing interest by adopting automated classification methods. Among these approaches, ensemble learning techniques have emerged as a promising solution by demonstrating their ability to enhance classification performance across diverse domains. However, traditional ensemble learning techniques may not deliver the desired accuracy improvements when applied to music datasets characterized by highly correlated low-level features associated with music genres. This study presents an innovative ensemble learning technique to address this challenge. The effectiveness of this approach is evaluated alongside established ensemble learning techniques by utilizing three publicly available music datasets, with two containing high-level sentiment-related features and one comprising low-level features extracted from music signals. The empirical experiments indicate that the proposed ensemble learning technique constantly outperforms conventional techniques in terms of classification accuracy. Notably, the proposed technique demonstrates remarkable performance enhancements when processing low-level features, whereas the traditional techniques failed to do so. This research highlights the substantial potential of advanced ensemble learning techniques in music genre classification and provides valuable insights into the strengths and limitations of various ensemble learning techniques when confronted with complex heterogeneous music datasets.

关键词： Contrastive Learning

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Correntropy-Based Bipartite Graph Factorization for Clustering 17th

Correntropy-Based Bipartite Graph Factorization for Clusteri...

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17th International Conference on Intelligent Robotics and Applications

作者： Yang, Shangzong Yang, Ben Wu, Jinghan Xue, Zhiyuan Zhang, Xuetao Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Xian 710049 Peoples R China Xi An Jiao Tong Univ Natl Engn Res Ctr Visual Informat & Applicat Xian 710049 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

Non-negative matrix factorization (NMF) is widely utilized in the domain of clustering, primarily due to its efficacy in decomposing the initial matrix into two smaller matrices, thereby facilitating the discernment of underlying data characteristics. Nevertheless, existing NMF-based methods still face two critical challenges: 1) the clustering efficiency is significantly affected by the original matrix's dimensionality. 2) in the presence of nonlinear and non-Gaussian noise and outliers, their robustness markedly declines. To tackle these issues, we propose a correntropy-based bipartite graph factorization model for clustering (CBGFC). First, a bipartite graph is constructed to capture the structure of samples, providing a more suitable representation. Then, by integrating the bipartite graph and NMF into a unified clustering framework, we avoid the efficiency being affected by the dimensionality of the data. Additionally, to improve the robustness of CBGFC, correntropy is introduced into the clustering model to handle noise and outliers. Extensive experiments demonstrate that CBGFC outperforms other state-of-the-art baselines in terms of clustering efficiency and robustness.

关键词： Clustering Non-negative Matrix Factorization Bipartite graph Correntropy

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A Novel Ocean Current Prediction Algorithm Based on Attention LSTM Neural Network 1

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17th International Conference on Intelligent Robotics and Applications

作者： Fu, Bowen Bai, Jibo Chen, Mingzhi Zhu, Daqi Univ Shanghai Sci & Technol Res Inst Lab Underwater Vehicles & Intelligent Sy Jungong Rd 516 Shanghai 200093 Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

For the movement of Autonomous Underwater Vehicles(AUVs), the impact of ocean currents is extremely important. Therefore, planning a navigation path that better conforms to the currents can greatly improve navigation efficiency. Although AUVs can measure nearby current changes with their sensors, it is impossible to measure the current speeds at all points in the entire sea area in real time while navigating, which means that a globally optimal path considering currents cannot be planned. In this paper, we propose an ocean current prediction method using Long Short-Term Memory (LSTM) networks with an attention mechanism to predict the current speed at any location in the sea area at any given time. The neural network is trained with data from the South China Sea. Experiments show that our method is more accurate compared to traditional LSTM and BP (Back Propagation) networks.

关键词： Ocean currents prediction Long short-Term memory Attention mechanism

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