Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su, Samer Alfayad
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引用次数: 0

Abstract

Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping intuition through operator-worn gloves with tactile-guided reinforcement learning. The framework's key innovation lies in its Tactile-Driven DCNN architecture-a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns-coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. Experimental results demonstrate reliable grasping performance across deformable and rigid objects while maintaining force precision critical for fragile targets. By bridging human teleoperation with autonomous tactile adaptation, RLMP eliminates dependency on visual input and predefined object models, establishing a new paradigm for robotic dexterity in occlusion-rich scenarios.

在非视觉环境中,使用多指手进行灵巧的机器人抓取仍然是一项严峻的挑战,因为在这种环境中,物体的几何形状和材料特性各不相同,需要自适应的力调制和触觉感知操作。为了解决这个问题,我们提出了基于强化学习的多模态感知(RLMP)框架,该框架通过操作员佩戴的手套将类似人类的抓取直觉与触觉引导的强化学习整合在一起。该框架的关键创新点在于其触觉驱动 DCNN 架构--一种轻量级卷积网络,利用时空压力模式实现了 98.5% 的物体识别准确率--并与一种 RL 策略完善机制相结合,将手指运动学与实时触觉反馈动态关联起来。实验结果表明,该系统对可变形物体和刚性物体都具有可靠的抓取性能,同时还能保持对易碎目标至关重要的力精度。通过在人类远程操作与自主触觉适应之间架起桥梁,RLMP 消除了对视觉输入和预定义物体模型的依赖,为机器人在多闭塞场景中的灵巧性建立了新的范例。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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