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Evolution in nature illustrates that the creatures' biological structure andtheir sensorimotor skills adapt to the environmental changes for survival.Likewise, the ability to morph and acquire new skills can facilitate anembodied agent to solve tasks of varying complexities. In this work, weintroduce a data-driven approach where effective hand designs naturally emergefor the purpose of grasping diverse objects. Jointly optimizing morphology andcontrol imposes computational challenges since it requires constant evaluationof a black-box function that measures the performance of a combination ofembodiment and behavior. We develop a novel Bayesian Optimization algorithmthat efficiently co-designs the morphology and grasping skills jointly throughlearned latent-space representations. We design the grasping tasks based on ataxonomy of three human grasp types: power grasp, pinch grasp, and lateralgrasp. Through experimentation and comparative study, we demonstrate theeffectiveness of our approach in discovering robust and cost-efficient handmorphologies for grasping novel objects.

Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects