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Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term. 

In spite of substantial progress, robust and dexterous in-hand manipulation remains a robotics grand challenge. Recent research has shown that optimization of robot hand morphology for specific tasks can result in custom hand designs that are low-cost, easy to maintain, and highly capable. However, the resulting manipulation strategies may not be very robust or generalizable in real-world situations. This paper shows that robustness can be improved dramatically by optimizing controls instead of contact force / trajectories and by considering uncertainty explicitly during the optimization process. We present a evolutionary algorithm based pipeline for co-optimizing hand morphology and control strategy over families of problems and initial states in order to achieve robust in-hand manipulation. We demonstrate that this approach produces robust results which utilize all surfaces of the hand and surprising dynamic motions. We showcase the advantage of optimizing joint limit values to create robust designs. Furthermore, we demonstrate that our approach is complementary to trajectory optimization based approaches and can be utilized to improve robustness of such results as well as to create custom hand designs from scratch. Results are shown for repositioning and reorienting diverse objects relative to the palm of the hand.