We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous manipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nearly 400 hand designs, testing grasp quality under external force disturbances. We validate the optimized designs in the real world through teleoperation of pickup and reorient manipulation tasks. Our real world evaluation, from over 900 teleoperated tasks, shows that the trend in design performance in simulation resembles that of the real world. Furthermore, we show that optimized hand designs from our approach outperform existing soft robot hands from prior work in the real world. The results highlight the usefulness of simulation in guiding parameter choices for anthropomorphic soft robotic hand systems, and the effectiveness of our automated design iteration approach, despite the sim-to-real gap.

翻译：我们通过协同优化设计与控制策略，在仿真环境中实现软体机器人手操作灵巧技能的自动化设计迭代。所提出的设计迭代流程融合遗传算法与策略迁移，为近400种手部设计学习控制策略，并在外力扰动下测试抓取质量。通过遥操作完成拾取与重定向操作任务，在真实世界中验证优化设计。基于900余次遥操作任务的真实世界评估表明，手部设计在仿真中的性能趋势与真实世界相似。此外，我们的优化手部设计在真实世界中优于现有先前工作的软体机器人手。研究结果凸显了仿真在引导仿人软体机器人手系统参数选择中的价值，以及我们的自动化设计迭代方法在克服仿真-现实差距后的有效性。