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Hierarchies of Planning and Reinforcement Learning for Robot Navigation

Hierarchies of Planning and Reinforcement Learning for Robot...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Woehlke, Jan Schmitt, Felix van Hoof, Herke Bosch Ctr Artificial Intelligence Renningen Germany Univ Amsterdam UvA Bosch Delta Lab Amsterdam Netherlands

ISBN: (纸本)9781728190778

Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high-level (HL) task representations, like a rough floor plan, are available. Previous work has demonstrated efficient learning by hierarchial approaches consisting of path planning in the HL representation and using sub-goals derived from the plan to guide the RL policy in the source task. However, these approaches usually neglect the complex dynamics and sub-optimal sub-goal-reaching capabilities of the robot during planning. This work overcomes these limitations by proposing a novel hierarchical framework that utilizes a trainable planning policy for the IIL representation. Thereby robot capabilities and environment conditions can be learned utilizing collected rollout data. We specifically introduce a planning policy based on value iteration with a learned transition model (VI-RL). In simulated robotic navigation tasks, VI-RL results in consistent strong improvement over vanilla RL, is on par with vanilla hierarchal RL on single layouts but more broadly applicable to multiple layouts, and is on par with trainable HL path planning baselines except for a parking task with difficult non-holonomic dynamics where it shows marked improvements.

关键词： Navigation Layout Reinforcement learning Path planning Data models Planning Task analysis

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Modeling the Variable Pitch Screws by Using the Envelope Theory Based on Cubic H-Bézier

Modeling the Variable Pitch Screws by Using the Envelope The...

引用

IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Yanwei Wu Gengbin Zhang Yu Ling Changqing Song Tie Zhang Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

This paper presents a novel approach to derive analytical expressions for variable pitch screw threads by employing three-segment composite H-Bézier curves as transitional curves at the beginning and end of the screw thread. Additionally, it introduces a methodology to represent the material surface as enveloping single-parameter surface families, enabling the calculation of the helical surface of variable pitch screw threads. The geometric model developed through this method finds applications in post-processing tasks such as material property analysis, numerical control machining, finite element analysis, and more. Finally, the proposed design and calculation methods for variable pitch screw threads are modeled and simulated within the MATLAB environment, demonstrating their practical value and feasibility.

关键词： Shape Design methodology Geometric modeling Machining Fasteners Harmonic analysis Polynomials Numerical models Trajectory Optimization

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6-DoF Contrastive Grasp Proposal Network

6-DoF Contrastive Grasp Proposal Network

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhu, Xinghao Sun, Lingfeng Fan, Yongxiang Tomizuka, Masayoshi Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA FANUC Amer Corp FANUC Adv Res Lab Union City CA USA

ISBN: (纸本)9781728190778

Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is challenging due to the complex object shape, incomplete object information, and sensor noise. In this paper, we present a 6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a single-view depth image. First, an image encoder is used to extract the feature map from the input depth image, after which 3-DoF grasp regions are proposed from the feature map with a rotated region proposal network. Feature vectors that within the proposed grasp regions are then extracted and refined to 6-DoF grasps. The proposed model is trained offline with synthetic grasp data. To improve the robustness in reality and bridge the simulation-to-real gap, we further introduce a contrastive learning module and variant image processing techniques during the training. CGPN can locate collision-free grasps of an object using a single view depth image within 0.5 second. Experiments on a physical robot further demonstrate the effectiveness of the algorithm. The experimental videos are available at [1].

关键词： Training Shape Robot vision systems Feature extraction Robustness Data models Planning

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Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks

Meta-Adversarial Inverse Reinforcement Learning for Decision...

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IEEE international conference on robotics and automation (ICRA)

作者： Wang, Pin Li, Hanhan Chan, Ching-Yao Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781728190778

Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of data to train a decent model on a particular task, and the model often fails to generalize to new tasks that have a different distribution. In practice, demonstrations from new tasks will be continuously observed and the data might be unlabeled or only partially labeled. Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available. In this work, we build an adaptable imitation learning model based on the integration of Meta-learning and Adversarial Inverse Reinforcement Learning (Meta-AIRL). We exploit the adversarial learning and inverse reinforcement learning mechanisms to learn policies and reward functions simultaneously from available training tasks and then adapt them to new tasks with the meta-learning framework. Simulation results show that the adapted policy trained with Meta-AIRL can effectively learn from limited number of demonstrations, and quickly reach the performance comparable to that of the experts on unseen tasks.

关键词： Training Adaptation models Simulation Frequency-domain analysis conferences Decision making Reinforcement learning

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Trajectory Planning of Differential Drive Robots for Auto-Docking Stations Using LSTM-Based Learning models 1

Trajectory Planning of Differential Drive Robots for Auto-Do...

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1st international conference on Smart Mechatronics, ICSMech 2024

作者： Saputra, Hendri Maja Hadiningrat, Alamsyah Ahmad H. Baskoro, Catur Hilman A.H.B. Nor, Nur Safwati Mohd Mirdanies, Midriem Ismail, Nanang National Research and Innovation Agency Brin Research Center for Smart Mechatronics Bandung40135 Indonesia Uin Sunan Gunung Djati Bandung Department of Electrical Engineering Bandung Indonesia Universiti Teknologi Malaysia Utm Faculty of Mechanical Engineering Johor Bahru81310 Malaysia

ISBN: (纸本)9798350368918

This paper presents the development of an auto-docking system using LSTM-based learning for trajectory planning on a differential drive robot, using data from ArUco markers to predict linear and angular velocities. The model relies on x-axis, y-axis, and yaw orientation data from ArUco to maintain stable navigation towards the auto-docking station. The experimental results show that the training accuracy for linear velocity is 87.2% and for angular velocity is 92.48%, with a Mean Squared Error (MSE) of 0.0003145 m/s for linear velocity and 0.1206 rad/s for angular velocity. In addition, this study also shows the power consumption obtained when the car is automatically docking, with an average battery power consumption of 83.05 watts and an average consumption on all motors of 30.59 watts. The results of this study indicate that the auto-docking system using LSTM-based learning for trajectory planning with ArUco marker position input can be applied to real-world conditions, thus contributing to the advancement of trajectory planning in mobile robots. © 2024 IEEE.

关键词： Mean square error

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That’s My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation

That’s My Point: Compact Object-centric LiDAR Pose Estimati...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Georgi Pramatarov Matthew Gadd Paul Newman Daniele De Martini Mobile Robotics Group (MRG) University of Oxford

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.

关键词： Point cloud compression Measurement Laser radar Three-dimensional displays Accuracy Semantics Pose estimation

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Key Parameters Prediction for Intelligent Control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd international conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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Path Planning of Unmanned Underwater Vehicles in Ocean Current Environment based on Safe Reinforcement Learning Algorithm

Path Planning of Unmanned Underwater Vehicles in Ocean Curre...

引用

IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Yu Wang Yongli Hu Zhenzhong Chu Logistics Engineering College Shanghai Maritime University Shanghai China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

This article aims to explore path planning for unmanned underwater vehicles (UUVs) using safety reinforcement learning (SRL) algorithms. Specifically, this article implements an SRL algorithm model that can adapt to ocean currents by setting reasonable state space, action space, reward function, and introducing safety constraints based on a real UUV. UUV path planning experiments were conducted in the Gazebo simulation environment using the ROS communication system. Additionally, a wireless communication module was designed for the real UUV. This article provides valuable experience for the research and practical application of UUV path planning based on SRL algorithms.

关键词： Wireless communication Autonomous underwater vehicles Adaptation models Mechatronics Oceans Reinforcement learning Path planning Underwater navigation Safety robotics and automation

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A Robotic Defect Inspection System for Free-form Specular Surfaces

A Robotic Defect Inspection System for Free-form Specular Su...

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IEEE international conference on robotics and automation (ICRA)

作者： Huo, Shengzeng Navarro-Alarcon, David Chik, David T. W. Hong Kong Polytech Univ KLN Hong Kong Peoples R China Hong Kong Appl Sci & Technol Res Inst Hong Kong Peoples R China

ISBN: (纸本)9781728190778

In this paper, we present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning. Given the mesh model of the workpiece, we implement K-means based region segmentation algorithm on the point cloud after preprocessing. Then, we take the smooth regions as input to plan the scanning path. A projection registration method that robustly localizes the object in the robot's frame is proposed for real-time workpiece localization. According to the optical features of the high-resolution line scan, we design an image processing pipeline to detect defects from the captured images. We report a detailed experimental study to validate the proposed methodology.

关键词： Image segmentation Analytical models Robot vision systems Pipelines Manipulators Optical imaging Real-time systems

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Analysis of the methods of Solving the Firing Data on Move

Analysis of the Methods of Solving the Firing Data on Move

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

作者： Huahuan Zhu Xiaoyu Zhang Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665475495

This paper takes armored vehicles fire control system as an example, based on the firing table fitting function, this paper discusses the method of calculating the firing elements in the angular rate fire control system and the line rate fire control system. In this paper, the advantages and disadvantages of the traditional method, the angular velocity method and the relative motion method, are compared, and a new method, the combined velocity method, is proposed in the on-line rate fire control system. The accuracy and practicability of the combined velocity method are proved by theoretical analysis and MATLAB simulation.

关键词： Analytical models automation Firing Projectiles Fitting Control systems Angular velocity

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