The study focuses on leveraging analytic inverse kinematics to address complex nonlinear constraints in bimanual robot manipulation. It introduces a parametrization method to simplify configuration space, enabling more effective motion planning algorithms. The research explores various planning techniques and their applications in bimanual tasks, emphasizing the importance of task-space constraints. By utilizing convex sets and trajectory optimization, the proposed approach aims to enhance efficiency and accuracy in constrained motion planning for robotic systems.
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