Robotics and Autonomous Systems最新文献_第4页

An Efficient Geometry-Informed Inverse Kinematics of a 7 DOF Cable-Driven Manipulator with Non-Sphere Shoulder and Wrist 具有非球面肩腕的7自由度缆索驱动机械臂的高效几何信息逆运动学

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-08 DOI: 10.1016/j.robot.2026.105335

Zhiwei Wu , Lei Yan , Tianhong Cheng , Wenfu Xu , Bin Liang

The cable-driven redundant manipulator (CDRM) is characterized by its lightweight, low inertia, and inherent compliance, enabling a wide range of applications in fields such as home services and medical rehabilitation. However, due to its complicated cable drive and transmission mechanism, compared with traditional redundant manipulator, the additional coupling cable kinematics is introduced into the inverse kinematics. Further, as the number of coupled equivalent joints of CDRM increases, it becomes challenging to obtain an efficient as well as stable inverse kinematics solution. In this paper, we propose an efficient geometry-informed inverse kinematics method by combining the geometry-based analytical approach and gradient-based numerical approach. First, the CDRM with 11 equivalent kinematic joints is reconstructed into a 7-DOF manipulator without joint offset. Based on the geometric characteristics, the analytical inverse kinematics of the reconstructed offset-free manipulator is derived to provide physically explainable iterative initial values in approximate solution space for numerical approach. Several numerical calculation results demonstrate that our method inherits the advantages of analytical approach, achieving accurate IK solutions, and improving the computational efficiency and the number of feasible solutions. Additionally, it also addresses the divergence issue resulting from irrational selection of initial values in numerical approach. Furthermore, the solution space can be comprehensively exploited by intuitively adjusting the arm-shape parameters and optimizing the manipulator’s configuration, in order to avoid surrounding obstacles, and optimize cable-tension distribution.

电缆驱动冗余机械手（CDRM）具有重量轻、惯性小、固有顺应性强等特点，在家庭服务、医疗康复等领域有着广泛的应用。然而，由于其缆索驱动和传动机构复杂，与传统的冗余度机械手相比，将附加的耦合缆索运动学引入了逆运动学中。此外，随着CDRM耦合等效关节数量的增加，获得高效且稳定的逆运动学解变得具有挑战性。本文将基于几何的解析方法和基于梯度的数值方法相结合，提出了一种有效的几何信息逆运动学方法。首先，将具有11个等效运动关节的CDRM重构为无关节偏移的7自由度机械臂；基于其几何特征，导出了重构无偏移机械手的解析运动学逆解，为数值求解提供了近似解空间中物理上可解释的迭代初值。数值计算结果表明，该方法继承了解析方法的优点，获得了精确的IK解，提高了计算效率和可行解的数量。此外，还解决了数值方法中由于初始值选择不合理而产生的发散问题。此外，通过直观地调整臂形参数和优化机械手构型，可以综合开发解空间，以避开周围障碍物，优化索张力分布。

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引用次数: 0

Obstacle crossing in revolute and prismatic knee underactuated biped robots 旋转和移动膝关节欠驱动双足机器人的越障研究

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-08 DOI: 10.1016/j.robot.2026.105340

Krishnendu Roy , R. Prasanth Kumar

Obstacle crossing is an important ability in biped and humanoid robots that are designed to traverse unstructured terrain. We consider the problem of determining the maximum (a) height, (b) width, (c) cross-sectional area, (d) thin vertical barrier height, and (e) square area of the obstacle that an underactuated biped robot with point-feet can cross while walking slowly. Two different biped robot configurations are compared for obstacle crossing: revolute knee and prismatic knee. The path needed to overcome the obstacle without touching it is determined with the help of binary occupancy grid in the sagittal plane and using genetic algorithm based maximization for each of the five cases, considering thin links as well as thick links for the biped robots. The determined collision free path for obstacle crossing is implemented as a trajectory and demonstrated in dynamic simulation in Mujoco simulation environment. In order to control the position of zero moment point (ZMP) and the ground projection of center of mass for stability, a reaction wheel in the torso is utilized. It is observed that increasing the thicknesses of the biped robot links in general has an effect of reducing the maximum size of the obstacle that can be crossed. Further, prismatic knee biped robot performs better than revolute knee biped robot in crossing large obstacles, especially with thick links. Experiments on a prismatic-knee biped robot further validate the results of GA and MuJoCo simulations.

越障是两足机器人和类人机器人在穿越非结构化地形时的一项重要能力。我们考虑的问题是确定(a)高度，(b)宽度，(c)横截面积，(d)薄垂直障碍物高度，(e)点足双足机器人在缓慢行走时可以穿过的障碍物的平方面积。比较了两种不同的双足机器人构型：旋转膝关节和移动膝关节。通过矢状面上的二元占用网格，结合两足机器人的细连杆和粗连杆，利用基于遗传算法的最大化方法，确定了在不接触障碍物的情况下克服障碍物所需的路径。确定的无碰撞过障路径以轨迹形式实现，并在Mujoco仿真环境中进行了动态仿真验证。为了控制零力矩点的位置和质心的地面投影以保持稳定性，在躯干上设置了反作用轮。我们观察到，一般来说，增加双足机器人连杆的厚度会减小可穿越障碍物的最大尺寸。此外，移动膝关节双足机器人在穿越大型障碍物，特别是粗连杆障碍物时，表现优于旋转膝关节双足机器人。在棱镜膝关节双足机器人上的实验进一步验证了遗传算法和MuJoCo仿真的结果。

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引用次数: 0

Dynamic merging and splitting for large-scale swarm navigation of UAVs and UGVs in unknown area 未知区域无人机与ugv大规模群导航的动态合并与分裂

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-08 DOI: 10.1016/j.robot.2026.105337

Jiang Zhao , Shukun Chen , Pei Chi , Yingxun Wang

This paper addresses the challenge of achieving efficient collaborative navigation and obstacle avoidance for aerial-ground unmanned swarm in unknown area by proposing a dynamic merge and split method. Firstly, a UAV-dominated collaborative decision-making framework is proposed, which includes a UAV allocation method driven by environmental information and a UGV planning and allocation method guided by UAVs. This framework enables collaborative and dynamic decision-making for the merge and split of the aerial-ground unmanned swarm. Secondly, an integrated dual-model and self-organizing motion control scheme for the aerial-ground unmanned swarm is designed. For the UAV swarm, a tracking–navigation dual-model motion control is developed to enhance robustness and sensing efficiency in unknown area. For the ground vehicles, a dynamic-boundary-based self-organizing motion control is proposed to leverage their high autonomy. Collectively, these control strategies yield highly efficient and coordinated motion control for the aerial-ground unmanned swarm. Finally, numerical simulation experiments are conducted and performance metrics are designed to compare the proposed method with existing representative approaches. The results indicate that the advantages of the proposed method gradually become more pronounced as the swarm scale increases.

本文提出了一种动态合并与分裂方法，解决了未知区域中地空无人机群高效协同导航与避障的难题。首先，提出了以无人机为主导的协同决策框架，包括环境信息驱动下的无人机分配方法和无人机引导下的UGV规划分配方法。该框架实现了空中-地面无人机群合并和分裂的协作和动态决策。其次，设计了一种集成的双模型自组织无人机群运动控制方案。针对无人机群，提出了一种跟踪-导航双模型运动控制方法，提高了未知区域的鲁棒性和感知效率。针对地面车辆的高自主性，提出了一种基于动态边界的自组织运动控制方法。总的来说，这些控制策略为空中-地面无人蜂群提供了高效和协调的运动控制。最后，进行了数值模拟实验，并设计了性能指标，将所提出的方法与已有的代表性方法进行了比较。结果表明，随着群体规模的增大，该方法的优势逐渐显现。

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引用次数: 0

Novel prescribed performance control for a kind of back-support exoskeleton with time delays 一种具有时滞的背托式外骨骼的新型规定性能控制

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-08 DOI: 10.1016/j.robot.2026.105350

Xiaogang Li , Yuan Ke

Back-support exoskeletons (BSEs) offer significant potential for reducing lumbar load and preventing lower-back injuries; however, their control is complicated by strong inter-channel coupling, time delays in actuation and sensing, and uncertainties induced by human–robot interaction. This paper develops a unified dynamic–kinematic model for a multi-input multi-output BSE that explicitly accounts for coupling effects, compliance, and delay characteristics, providing a control-oriented and physically consistent foundation. Based on this model, a prescribed performance control (PPC) framework is proposed to guarantee bounded tracking errors with predefined transient and steady-state behaviour in the presence of multidimensional, time-varying delays, without requiring model decoupling. To enhance robustness against lumped uncertainties, a hybrid observer integrating a linear extended state observer and a sliding mode observer is designed for real-time disturbance estimation and compensation. Simulation results obtained on a biomechanically realistic BSE platform demonstrate that the proposed PPC–LESO–SMO scheme achieves superior tracking accuracy, robustness, and convergence speed compared with conventional PPC and existing observer-based control approaches.

背部支撑外骨骼（bse）提供显著的潜力，以减少腰椎负荷和防止腰背部损伤；然而，由于通道间的强耦合、驱动和传感的时间延迟以及人机交互引起的不确定性，它们的控制变得复杂。本文为多输入多输出BSE建立了一个统一的动态运动学模型，该模型明确地考虑了耦合效应、顺应性和延迟特性，为面向控制和物理一致提供了基础。在此模型的基础上，提出了一种规定性能控制（PPC）框架，以保证在存在多维时变延迟的情况下，具有预定义的瞬态和稳态行为的有界跟踪误差，而不需要模型解耦。为了增强对集总不确定性的鲁棒性，设计了一种集成线性扩展状态观测器和滑模观测器的混合观测器，用于实时干扰估计和补偿。仿真结果表明，与传统的PPC和现有的基于观测器的控制方法相比，所提出的PPC - leso - smo方案具有更好的跟踪精度、鲁棒性和收敛速度。

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引用次数: 0

RoCeDiRNet-3DoF: Robust center direction network for cloth grasping point localization in complex scenes RoCeDiRNet-3DoF：基于鲁棒中心方向网络的复杂场景布料抓取点定位

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-08 DOI: 10.1016/j.robot.2026.105328

Jiaxiang Luo, Yufan Hu

Cloth grasping poses a fundamental challenge in robotics and computer vision, and constitutes a key capability for service robots. Accurate and robust localization of fabric grasping points is a critical prerequisite for enabling efficient and dexterous fabric manipulation. While recent deep learning approaches have achieved promising results on standard benchmarks, their performance still degrades substantially in complex scenarios involving severe folding, occlusion, and unknown backgrounds. This limitation primarily arises from insufficient global contextual modeling in existing dense regression frameworks, which rely heavily on local feature extraction while lacking the ability to capture global dependencies crucial for understanding fabric deformation patterns. To address these challenges, we propose RoCeDiRNet-3DoF, a novel framework built upon an InceptionNeXt encoder and a Wavelet Multiscale Convolutional Attention Decoder (WMCAD). WMCAD adopts a three-stage hierarchical architecture, incorporating Wavelet Convolution Blocks for global feature extraction, Dynamic Wavelet Upsampling Blocks to preserve semantic details during interpolation, and Multi-scale Mixed Attention Gates for effective cross-layer feature fusion. By leveraging WMCAD’s enhanced global feature modeling, RoCeDiRNet-3DoF achieves superior performance in challenging scenarios, achieving state-of-the-art (SOTA) results on the ViCoS dataset with an F1 score of 82.6%. Furthermore, across various complex scenario configurations, RoCeDiRNet-3DoF consistently outperforms competing methods, representing the current optimal solution for this task. The source code is available at: https://github.com/hyf381752569-stack/RoCeDiRNet-3DoF.

布料抓取是机器人技术和计算机视觉领域的一个基本挑战，也是服务机器人的一项关键能力。织物抓握点的准确和鲁棒定位是实现高效灵巧织物操作的关键前提。虽然最近的深度学习方法在标准基准上取得了可喜的结果，但在涉及严重折叠、遮挡和未知背景的复杂场景下，它们的性能仍然会大幅下降。这种限制主要源于现有密集回归框架中缺乏全局上下文建模，这些框架严重依赖于局部特征提取，而缺乏捕获全局依赖关系的能力，这对于理解织物变形模式至关重要。为了解决这些挑战，我们提出了RoCeDiRNet-3DoF，这是一个基于InceptionNeXt编码器和小波多尺度卷积注意力解码器（WMCAD）的新框架。WMCAD采用三阶段分层结构，采用小波卷积块进行全局特征提取，动态小波上采样块在插值过程中保留语义细节，多尺度混合注意门进行有效的跨层特征融合。通过利用WMCAD增强的全局特征建模，RoCeDiRNet-3DoF在具有挑战性的场景中取得了卓越的性能，在ViCoS数据集上获得了最先进的（SOTA）结果，F1得分为82.6%。此外，在各种复杂的场景配置中，RoCeDiRNet-3DoF始终优于竞争对手的方法，代表了该任务的当前最佳解决方案。源代码可从https://github.com/hyf381752569-stack/RoCeDiRNet-3DoF获得。

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引用次数: 0

Collision cone based time-efficient method for 3D collision avoidance for UAVs: A purely heading-based solution 基于碰撞锥的无人机三维避碰方法：一种纯粹基于航向的解决方案

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-07 DOI: 10.1016/j.robot.2026.105332

Manaram Gnanasekera, Jay Katupitiya

The increasing deployment of unmanned aerial vehicles (UAVs) across various fields, from agriculture to disaster management, has raised critical concerns about mid-air collisions in increasingly congested airspaces. While previous research has extensively explored collision avoidance techniques, most solutions focus either on static or low-density dynamic environments, leaving a gap in addressing UAV navigation in densely cluttered, dynamic 3D environments. This paper introduces a novel collision cone-based approach designed to enhance time-efficiency and precision in 3D UAV collision avoidance scenarios, particularly in complex and dynamic environments with multiple obstacles. Through both simulation and real-world experiments, the method demonstrates superior time-efficiency compared to a benchmark method, while maintaining robust performance in unpredictable environments. The contributions of this work include the development of a real-time adaptable algorithm that recalculates optimal paths based on dynamic changes and its practical validation in realistic, high-density scenarios. This work fills a significant research gap by addressing the limitations of previous 2D approaches and static obstacle methods, providing a comprehensive solution for UAVs operating in highly dynamic 3D spaces.

从农业到灾害管理等各个领域越来越多地部署无人驾驶飞行器（uav），这引起了人们对日益拥挤的空域中空中碰撞的严重担忧。虽然之前的研究已经广泛探索了避碰技术，但大多数解决方案都集中在静态或低密度动态环境上，这在解决密集混乱的动态3D环境下的无人机导航问题上留下了空白。本文介绍了一种新的基于碰撞锥的方法，旨在提高三维无人机避碰场景的时效性和精度，特别是在具有多个障碍物的复杂动态环境中。通过仿真和实际实验，与基准方法相比，该方法具有更高的时间效率，同时在不可预测的环境中保持稳健的性能。这项工作的贡献包括开发了一种基于动态变化重新计算最优路径的实时自适应算法，并在现实的高密度场景中进行了实际验证。这项工作通过解决以前的2D方法和静态障碍方法的局限性，填补了一个重要的研究空白，为无人机在高动态3D空间中运行提供了一个全面的解决方案。

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引用次数: 0

Unified motion control of 4WIS-4WID WMR with unlimited steering and load transfer consideration 4wi - 4wid WMR的统一运动控制，具有无限转向和负载转移考虑

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-07 DOI: 10.1016/j.robot.2026.105341

Dongwoo Seo, Seokki Moon, Jaeyoung Kang

This paper presents a unified motion control framework for a four-wheel independent steering (4WIS) and four-wheel independent drive (4WID) wheeled mobile robot (WMR) equipped with an unlimited steering angle system. Unlike conventional methods that rely on mode-specific kinematic controllers, the proposed controller does not require prior classification of driving modes. The controller defines the necessary forces at the tire positions to track the desired velocity profile in the body-fixed frame, considering tire slip dynamics, load transfer effects, and friction constraints based on the Magic Formula. The steering angles and in-wheel motor torques are then determined to generate the required forces. Due to the difficulty of directly measuring tire forces, a disturbance observer (DOB) is used to estimate these forces in real-time. Simulation results demonstrate that the proposed controller outperforms conventional kinematics-based approaches in velocity tracking accuracy, while maintaining stable tire force distribution and vertical load limits, ensuring the robot’s stability and effective maneuverability even under highly dynamic conditions characterized by large lateral accelerations and the resulting tire slip and load transfer.

提出了一种具有无限转向角系统的四轮独立转向（4WIS）和四轮独立驱动（4WID）轮式移动机器人（WMR）的统一运动控制框架。与依赖于特定模式的运动学控制器的传统方法不同，所提出的控制器不需要事先对驱动模式进行分类。考虑轮胎滑移动力学、载荷传递效应和基于Magic Formula的摩擦约束，控制器定义轮胎位置所需的力以跟踪车身固定框架中所需的速度轮廓。然后确定转向角度和轮内电机扭矩以产生所需的力。由于难以直接测量轮胎受力，采用扰动观测器（DOB）实时估计轮胎受力。仿真结果表明，该控制器在速度跟踪精度方面优于传统的基于运动学的方法，同时保持稳定的轮胎力分布和垂直负载限制，即使在具有大横向加速度和由此导致的轮胎打滑和负载转移的高动态条件下，也能确保机器人的稳定性和有效的机动性。

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引用次数: 0

Vision-guided gripping process with minimizing folding for flexible fabric materials by integrating a sequential optimization algorithm and FEM analysis 结合序列优化算法和有限元分析的视觉引导柔性织物材料最小折叠夹持过程

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-07 DOI: 10.1016/j.robot.2026.105349

Minh Khang Ngo , Chanhee Won , HyunKyo Lim , Dae Young Lim , Than Trong Khanh Dat , Jonghun Yoon

Automatic fabric handling in garment manufacturing presents significant challenges due to the soft, deformable, and highly variable nature of textile materials. This paper proposes an integrated robotic fabric gripping system capable of reliably identifying and manipulating fabric parts with minimal folding. The system comprises an industrial robotic arm, four needle grippers mounted on an adjustable jig mechanism, and a high-precision 3D vision camera for real-time fabric detection. A key contribution of this work is a sequential optimization model that determines four optimal gripping points for each fabric pattern, based on material characterization, deformation criteria, and folding definitions derived from experiments and finite element simulations. These points are mapped relative to CAD data and stored for retrieval. A vision-based matching algorithm then aligns real-time image inputs with CAD templates to localize the fabric piece and recover the precomputed optimal gripping points, which are transmitted to the robot for autonomous execution. Quantitative evaluations demonstrate that the proposed approach significantly reduces fabric folding and enhances the reliability of robotic garment handling, representing a substantial step toward fully automated garment production.

由于纺织材料的柔软，可变形和高度可变的性质，服装制造中的自动织物处理提出了重大挑战。提出了一种集成的机器人织物抓取系统，该系统能够以最小的折叠量可靠地识别和操纵织物零件。该系统包括一个工业机械臂、安装在可调夹具机构上的四个抓针器和一个用于实时织物检测的高精度3D视觉相机。这项工作的一个关键贡献是一个顺序优化模型，该模型基于材料特性、变形标准和从实验和有限元模拟中得出的折叠定义，确定每种织物图案的四个最佳夹紧点。这些点相对于CAD数据进行映射并存储以供检索。然后，基于视觉的匹配算法将实时图像输入与CAD模板对齐，以定位织物片并恢复预先计算的最佳抓取点，这些点将传输给机器人进行自主执行。定量评估表明，所提出的方法显著减少了织物折叠，提高了机器人服装处理的可靠性，代表着向全自动服装生产迈出了实质性的一步。

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引用次数: 0

Path planning of bionic robotic fish based on enhanced SAC algorithm combined with HER and CQL 基于改进SAC算法结合HER和CQL的仿生机器鱼路径规划

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-06 DOI: 10.1016/j.robot.2026.105336

Ming Wang, Tinglong Zhao, Lingchen Zuo, Yanling Gong, Guangxin Lv, Ruilong Wang

Underwater robots play a crucial role in ocean exploration and resource development. As a type of underwater robot, bionic robotic fish can mimic the swimming patterns of real fish, offering unique advantages in complex underwater environments. However, traditional path planning methods perform poorly in unknown or dynamic environments. This paper proposes a path planning algorithm based on deep reinforcement learning, which improves the path planning capabilities of bionic robotic fish in unknown environments by combining the Soft Actor-Critic (SAC) algorithm with the Hindsight Experience Replay (HER) mechanism. Furthermore, the use of the Conservative Q-Learning (CQL) algorithm enhances the algorithm’s exploration capability and robustness. The effectiveness and practicality of the proposed algorithm are validated through simulation results.

水下机器人在海洋勘探和资源开发中发挥着至关重要的作用。仿生机器鱼作为一种水下机器人，能够模仿真鱼的游动模式，在复杂的水下环境中具有独特的优势。然而，传统的路径规划方法在未知或动态环境中表现不佳。本文提出了一种基于深度强化学习的路径规划算法，通过将软Actor-Critic （SAC）算法与后见之明经验回放（HER）机制相结合，提高了仿生机器鱼在未知环境下的路径规划能力。此外，使用保守Q-Learning （CQL）算法增强了算法的探索能力和鲁棒性。仿真结果验证了该算法的有效性和实用性。

引用次数: 0

A self-supervised exploratory guided push and grasp approach to enhance robotic manipulation in clutter 一种自监督探索性引导推抓方法提高机器人在杂波环境下的操作能力

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-01-06 DOI: 10.1016/j.robot.2026.105339

Chiat Pin Tay , Shijun Yan , Chong Chen , Wei Qi Toh , Miaolong Yuan , Dongkyu Choi

Robot manipulation of cluttered and stacked objects remains a crucial yet challenging task with immense real-world applications. Integrating push actions into grasp strategies has been proposed to address this challenge. However, existing deep learning approaches, particularly those based on reinforcement learning, often suffer from lengthy training times and inefficient action selection. This paper introduces a novel exploratory guided push approach that leverages self-supervised learning and a memory buffer enhancement strategy to accelerate efficient data sampling for model training. We investigated and analysed various network architectures, data collection methods, and learning strategies to understand their impacts on push-and-grasp tasks. Extensive experiments conducted in both simulated and real environments demonstrate the effectiveness of our proposed solution.

机器人操纵杂乱和堆叠的物体仍然是一个具有巨大现实应用的关键但具有挑战性的任务。为了应对这一挑战，建议将推动行动整合到把握策略中。然而，现有的深度学习方法，尤其是那些基于强化学习的方法，往往存在训练时间长、动作选择效率低的问题。本文介绍了一种新的探索性引导推送方法，该方法利用自监督学习和记忆缓冲增强策略来加速模型训练的有效数据采样。我们调查并分析了各种网络架构、数据收集方法和学习策略，以了解它们对推抓任务的影响。在模拟和真实环境中进行的大量实验证明了我们提出的解决方案的有效性。

引用次数: 0