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

Pan, Xinlei and Garg, Animesh and Anandkumar, Animashree and Zhu, Yuke (2020) Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemProject website
Garg, Animesh0000-0003-0482-4296
Zhu, Yuke0000-0002-9198-2227
Additional Information:We gratefully acknowledge the feedback from members in NVIDIA AI Algorithms research team. We also acknowledge Jonathan Tremblay from NVIDIA for the support on ViSII rendering.
Record Number:CaltechAUTHORS:20210225-132725054
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108206
Deposited By: George Porter
Deposited On:26 Feb 2021 15:16
Last Modified:26 Feb 2021 15:16

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