  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.
