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Kolmogorov-Arnold Networks for Online Reinforcement Learning

Kolmogorov-Arnold Networks for Online Reinforcement Learning

引用

international conference on Control, automation and Systems ( ICCAS)

作者： Victor A. Kich Jair A. Bottega Raul Steinmetz Ricardo B. Grando Ayano Yorozu Akihisa Ohya Intelligent Robot Laboratory University of Tsukuba Tsukuba Japan Technology Center Federal University of Santa Maria Santa Maria Brazil Robotics and AI Lab Technological University of Uruguay Rivera Uruguay

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks, providing universal function approximation with fewer parameters and reduced memory usage. In this paper, we explore the use of KANs as function approximators within the Proximal Policy Optimization (PPO) algorithm. We evaluate this approach by comparing its performance to the original MLP-based PPO using the DeepMind Control Proprio robotics benchmark. Our results indicate that the KAN-based reinforcement learning algorithm can achieve comparable performance to its MLP-based counterpart, often with fewer parameters. These findings suggest that KANs may offer a more efficient option for reinforcement learning models. Our implementations can be found in the following link: https://***/victorkich/Kolmogorov-PPO.

关键词： Neural networks Memory management Reinforcement learning Approximation algorithms Real-time systems Computational efficiency Function approximation Splines (mathematics) Optimization Robots

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Modeling and Route Planning for Collaborative Multi-Agent Inspection

Modeling and Route Planning for Collaborative Multi-Agent In...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jinbo Wang Jia Xu Yuanqiang Zhou Li Li Shuai Sui Yuanxin Li Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Liaoning University of Technology Ji nzhou China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In various practical applications, collaborative inspection systems in which multiple agents work together to accomplish inspection tasks are becoming increasingly important for enhancing operational efficiency. This paper addresses the routing problem in multi-agent collaborative inspection systems, where certain inspection points require the simultaneous presence of multiple agents to perform inspection operations. A novel approach using max-plus algebra is presented to model the collaborative inspection process and it provides a foundation for research and applications in system control and scheduling optimization. The max-plus linear (MPL) model is then converted into a Mixed Integer Linear Programming (MILP) formulation to tackle the routing problem with complex constraints inherent in the collaborative scene. Experimental results validate that the proposed MPL model and MILP approach reduce inspection completion time and waiting time at collaborative points.

关键词： Multiprotocol label switching Adaptation models Solid modeling Algebra Collaboration Inspection Routing Solids Real-time systems Robots

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Online Fault Detection in Manipulation Tasks via Generative models

Online Fault Detection in Manipulation Tasks via Generative ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Michael W. Lanighan Oscar Youngquist TRACLabs Inc. Houston TX

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper introduces a method, Generative Adversarial Networks for Detecting Erroneous Results (GANDER), leveraging Generative Adversarial Networks to provide online error detection in manipulation tasks for autonomous robot systems. GANDER relies on mapping input images of a trained task to a learned manifold that contains only positive task executions and outcomes. When reconstructed through this manifold, the input images from successful task executions will remain largely unchanged, while the images from a failed task will change significantly. Using this insight, GANDER enables inspection and task outcome verification capabilities using a large number of positive examples but only a small set of negative examples, thus increasing the applicability of autonomous robot systems. We detail the design of GANDER and provide results of a proof-of-concept system, establishing its efficacy in an autonomous inspection, maintenance, and repair task. GANDER produces favorable results compared to baseline approaches and is capable of correctly identifying off-nominal behavior with 91.65% accuracy in our test task. Ablation studies were also performed to quantify the amount of data ultimately needed for this approach to succeed.

关键词： Training Manifolds Accuracy Fault detection Transforms Inspection Generative adversarial networks

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Computer Aided Research on Efficient Simulation Prediction and Anti-Deformation Suppression Method for Aviation Panel Riveting Deformation

Computer Aided Research on Efficient Simulation Prediction a...

引用

automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Yonggang Chen Yonggang Kang Tianyu Wang Huan Xiao School of Mechanical Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665475495

Aviation panel structure is a typical assembly of aircraft. During the assembly process of metal panel, the inherent forming and connecting characteristics of the riveting process will cause macroscopic deformation such as local subsidence and overall bending moment of the panel, resulting in difficulty in the assembly and coordination of subsequent components. This paper takes the riveting process of the panel with L-shaped stringer applied in an aviation cabin as the research object, the deformation reasons of the L-shaped stringer aviation plate structure are analyzed, and the finite element solid model of the riveting process is established. On this basis, a temperature field equivalent model is proposed, which greatly improves the computational efficiency. Further, a set of anti-deformation active suppression methods for riveted assembly deformation of aeronautical panel structures are proposed for the actual assembly scene, then, the effectiveness of the method is verified by simulation examples and experimental studies.

关键词： Deformable models Solid modeling Analytical models Deformation Computational modeling Predictive models Data models

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Learning Peg-in-Hole Assembly Skills from Demonstration Based on Geometric Constraints 11

Learning Peg-in-Hole Assembly Skills from Demonstration Base...

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11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2021

作者： Tian, Zhonglai Cheng, Hongtai Zhao, Liang Cao, Shuo School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9781665425278

Existing Learning from Demonstration (LfD) methods that can learn the peg-in-hole assembly skills mainly adopts the impedance control strategy, and generally do not pay attention to the assembly skill segmentation problem. This scheme is susceptible to environmental noise and is difficult to learn stable and effective assembly skills. In this paper, a geometric constraints-based LfD method is proposed and applied to the peg-in-hole assembly task. By identifying geometric constraints from the motion trajectory and considering the contact force information, the demonstration dataset is first segmented into a series of different sub-skills. Hybrid Force/Position Control (HFPC) schema is adopted to model those skills. The selection matrix is identified according to the geometric constraints. The Gaussian Mixture Model (GMM)/Gaussian Mixture Regression(GMR) and quasiperiodic motion models are used to representing the reference trajectories. A switching mechanism is also learned to coordinate those HFPCs with different configurations to accommodate different sub-skills. The experimental results show that the proposed method can accomplish the learning and reproduction of assembly tasks stably and effectively. © 2021 IEEE.

关键词： Demonstrations

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A Collision-Aware Cable Grasping Method in Cluttered Environment

A Collision-Aware Cable Grasping Method in Cluttered Environ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lei Zhang Kaixin Bai Qiang Li Zhaopeng Chen Jianwei Zhang Department of Informatics TAMS (Technical Aspects of Multimodal Systems) Universität Hamburg Agile Robots AG Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce a Cable Grasping-Convolutional Neural Network (CG-CNN) designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition technique to dissect the non-convex cable model, with grasp quality autonomously labeled based on simulated grasping attempts. The CG-CNN is refined using this simulated dataset and enhanced through domain randomization techniques. Subsequently, the trained model predicts grasp quality, guiding the optimal grasp pose to the robot’s controller for execution. Grasping efficacy is assessed across both synthetic and real-world settings. Given our model’s implicit collision sensitivity, we achieved commendable success rates of 92.3% for known cables and 88.4% for unknown cables, surpassing contemporary state-of-the-art approaches. Supplementary materials can be found at https://***/cg-cnn/.

关键词： Sensitivity Neural networks MIMICs Grasping Predictive models Robot sensing systems Collision avoidance

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Personalized Autocomplete Teleoperation: Real-Time User Adaptation using Transfer Learning with Partial Feedback 11

Personalized Autocomplete Teleoperation: Real-Time User Adap...

引用

11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2021

作者： Hussein, Mohammad Haj Elhajj, Imad H. Asmar, Daniel Vision and Robotics Lab American University of Beirut Beirut Lebanon

ISBN: (纸本)9781665425278

Autocomplete enhances teleoperation by recognizing operator intentions and automating motions when instructed. This has shown to improve performance and reduce operator load. Traditionally, recognizing operator intended motions is accomplished using pre-trained deep learning (DL) models. In this paper, we introduce personalization to the Autocomplete Teleoperation Framework. By customize the Autocom-plete DL model using transfer learning with partial feedback. This approach allows the model to adapt to the specific operator in a relatively short period. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model. Results demonstrate the effectiveness of the proposed method in adapting to the specific operator. © 2021 IEEE.

关键词： Remote control

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Sugar Cane Yield Prediction Using Drone Data Processed by LSTM Algorithm

Sugar Cane Yield Prediction Using Drone Data Processed by LS...

引用

Mechatronics, Control and robotics (ICMCR), international conference on

作者： Kotetsu Arakawa Ryusei Shimizu Shota Kikuchi Genci Capi Graduate School of Science and Engineering Dept. of Mechanical Eng. Hosei University Tokyo Japan

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Sugarcane is an essential crop in southwestern Japan, particularly in Okinawa and Kagoshima, where it sustains local economies and supports large-scale sugar production. Traditional yield prediction relies on labor-intensive field surveys and is increasingly challenged by a shortage of skilled labor, resulting in potential inaccuracies. This work proposes an innovative approach to sugarcane yield prediction by combining Long Short-Term Memory (LSTM) models with Genetic Algorithms (GA) to optimize model performance. Drone-based data collection methods are also explored, leveraging aerial imagery to provide additional predictive features. The proposed model demonstrates the effectiveness of integrating time-series and spatial data, offering a scalable and accurate solution for improving yield forecasting and supporting efficient operations in sugarcane production.

关键词： Accuracy Three-dimensional displays Biological system modeling Production Predictive models Sugar industry Data models Long short term memory Genetic algorithms Drones

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Versatile Locomotion by Integrating Ankle, Hip, Stepping, and Height Variation Strategies

Versatile Locomotion by Integrating Ankle, Hip, Stepping, an...

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IEEE international conference on robotics and automation (ICRA)

作者： Ding, Jiatao Xin, Songyan Lam, Tin Lun Vijayakumar, Sethu Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Chinese Univ Hong Kong Shenzhen Peoples R China AIRS Shenzhen Peoples R China

ISBN: (纸本)9781728190778

Stable walking in real-world environments is a challenging task for humanoid robots, especially when considering the dynamic disturbances, e.g., caused by external perturbations that may be encountered during locomotion. The varying nature of disturbance necessitates high adaptability. In this paper, we propose an enhanced Nonlinear Model Predictive Control (NMPC) approach for robust and adaptable walking - we term it versatile locomotion, by limiting both the Center of Pressure (CoP) and Divergent Component of Motion (DCM) movements. Due to utilization of the Nonlinear Inverted Pendulum plus Flywheel model, the robot is endowed with the capabilities of CoP manipulation (if equipped with finite-sized feet), step location adjustment, upper body rotation, and vertical height variation. Considering the feasibility constraints, especially the usage of relaxed CoP constraints, the NMPC scheme is established as a Quadratically Constrained Quadratic Programming problem, which is solved efficiently by Sequential Quadratic Programming with enhanced solvability. Simulation experiments demonstrate the effectiveness of our method to recruit optimal hybrid strategies in order to realize versatile locomotion, for the robot with finite-sized or point feet.

关键词： Legged locomotion Limiting Perturbation methods Humanoid robots Flywheels Quadratic programming Task analysis

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Enhancing Human Motion Imitation in Humanoid Robots: A Comparative Study of ANN, ANFIS, and GA-Optimized ANFIS for Inverse Kinematics of the Surena-V Humanoid's Arm

Enhancing Human Motion Imitation in Humanoid Robots: A Compa...

引用

RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Amin Mozayyan Aghil Yousefi-Koma Alireza Naeini Sara Rahmati Kookandeh Center of Advanced Systems and Technologies (CAST) School of Mechanical Engineering University of Tehran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Humanoid robots are increasingly vital in automation, healthcare, and hazardous environments, where teleoperation enables them to replicate human movements for complex tasks. A key challenge in teleoperation is solving inverse kine-matics, which determines the joint angles required for a robotic arm to reach a specific end-effector position. This research addresses the challenge of solving the inverse kinematics of a redundant robotic arm of the Surena-V humanoid robot, to enhance the robot's capability to accurately imitate human arm movements. Using MediaPipe for human pose estimation, wrist coordinates were input into models to calculate joint angles. Three methods were deployed: Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and ANFIS optimized using a Genetic Algorithm (GA). The GA-optimized ANFIS model achieved the lowest Root Mean Square Error (RMSE) in joint angle prediction, with values of 6.93, 7.23, 12.33, and 0.9, and superior end-effector accuracy with RMSEs of 0.0199 m, 0.0150 m, and 0.0203 m for the x, y, and z coordinates. These results underscore the effectiveness of GA-optimized ANFIS in handling complex nonlinearities, offering significant improvements in humanoid robot performance for teleoperation and human-robot interaction tasks.

关键词： Wrist Adaptation models Accuracy Robot kinematics Humanoid robots Kinematics Artificial neural networks Robot sensing systems End effectors Genetic algorithms

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