软夹爪与抓取姿态联合优化，提升机器人抓握性能
Co-Design of Soft Gripper with Neural Physics
- 用神经物理模型在仿真中联合优化夹爪刚度分布与抓取姿态
- 硬件实验显示新设计抓握成功率显著高于基线方法
- 适用于需要自适应抓握的柔性机器人场景
在机器人操作中，控制器与末端执行器设计均至关重要。软夹爪可通过形变适应不同几何形状，但其设计与最优抓取姿态的确定仍具挑战。本文提出一种协同设计框架，利用在仿真中训练的神经物理模型，生成优化的软夹爪分块刚度分布及其最佳抓取姿态。我们推导出基于柔顺结构的均匀压力腱模型，并通过随机化夹爪姿态与设计参数生成多样化数据集。训练神经网络以近似正向仿真，获得快速、可微的代理模型。将该代理嵌入端到端优化流程，同时优化理想刚度配置与最优抓取姿态。最后，通过改变结构参数3D打印出不同刚度的优化夹爪。我们在仿真和硬件实验中均验证了所提设计显著优于基线方案。
原文摘要 · Abstract (English)
For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper and finding its grasp pose remains challenging. In this paper, we propose a co-design framework that generates an optimized soft gripper's block-wise stiffness distribution and its grasping pose, using a neural physics model trained in simulation. We derived a uniform-pressure tendon model for a flexure-based soft finger, then generated a diverse dataset by randomizing both gripper pose and design parameters. A neural network is trained to approximate this forward simulation, yielding a fast, differentiable surrogate. We embed that surrogate in an end-to-end optimization loop to optimize the ideal stiffness configuration and best grasp pose. Finally, we 3D-print the optimized grippers of various stiffness by changing the structural parameters. We demonstrate that our co-designed grippers significantly outperform baseline designs in both simulation and hardware experiments. More info: http://yswhynot.github.io/codesign-soft/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。