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  1. The 3D shape of a robot’s end-effector plays a critical role in determining it’s functionality and overall performance. Many of today’s industrial applications rely on highly customized gripper design for a given task to ensure the system’s robustness and accuracy. However, the process of manual hardware design is both costly and time-consuming, and the quality of the design is also dependent on the engineer’s experience and domain expertise, which can easily be out-dated or inaccurate. The goal of this paper is to use machine learning algorithms to automate this design process and generate task-specific gripper designs that satisfy a set of pre-defined design objectives. We model the design objectives by training a Fitness network to predict their values for a pair of gripper fingers and a grasp object. This Fitness network is then used to provide training supervision to a 3D Generative network that produces a pair of 3D finger geometries for the target grasp object. Our experiments demonstrate that the proposed 3D generative design framework generates parallel jaw gripper finger shapes that achieve more stable and robust grasps as compared to other general-purpose and task-specific gripper design algorithms. 
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  2. We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robotic systems have relied on centralized motion planners, whose run times often scale exponentially with team size, and thus, fail to handle dynamic environments with open-loop control. In this paper, we tackle this problem with multi-agent reinforcement learning, where a shared policy network is trained to control each individual robot arm to reach its target end-effector pose given observations of its workspace state and target end-effector pose. The policy is trained using Soft Actor-Critic with expert demonstrations from a sampling-based motion planning algorithm (i.e., BiRRT). By leveraging classical planning algorithms, we can improve the learning efficiency of the reinforcement learning algorithm while retaining the fast inference time of neural networks. The resulting policy scales sub-linearly and can be deployed on multi-arm systems with variable team sizes. Thanks to the closed-loop and decentralized formulation, our approach generalizes to 5-10 multiarm systems and dynamic moving targets (>90% success rate for a 10-arm system), despite being trained on only 1-4 arm planning tasks with static targets. 
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