论文标题
通过优化强大的各种物体的牢固掌握，紧急的手形态和控制
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
论文作者
论文摘要
自然界的进化表明，生物的生物结构及其感觉运动技能适应了生存的环境变化。同样，变形和获得新技能的能力可以促进体现的代理来解决不同复杂性的任务。在这项工作中，我们介绍了一种数据驱动的方法，其中有效的手工设计自然出现，以掌握各种物体。共同优化形态和控制会引起计算挑战，因为它需要持续评估黑框功能，该功能衡量了实施例和行为的组合的性能。我们开发了一种新型的贝叶斯优化算法，该算法通过学习的潜在空间表示有效地共同设计了形态和抓地技能。我们根据三种人类掌握类型的分类法设计了掌握任务：功率掌握，捏手和侧面掌握。通过实验和比较研究，我们证明了我们方法在发现掌握新物体的稳健和成本效益的手工形态方面的有效性。
Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to morph and acquire new skills can facilitate an embodied agent to solve tasks of varying complexities. In this work, we introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. Jointly optimizing morphology and control imposes computational challenges since it requires constant evaluation of a black-box function that measures the performance of a combination of embodiment and behavior. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills jointly through learned latent-space representations. We design the grasping tasks based on a taxonomy of three human grasp types: power grasp, pinch grasp, and lateral grasp. Through experimentation and comparative study, we demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
