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RAPTOR

RAPid and robust Trajectory Optimization for Robots

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We present "RAPid and robust Trajectory Optimization toolbox for Robots" (RAPTOR) -- Dynamic locomotion for humanoid robots presents significant analytical and computational challenges due to the extensive number of linkages and degrees of freedom. This complexity results in a vast search space for feasible gaits which translates into a time-consuming process when optimizing over trajectories. In addition, the process often involves numerous hyperparameters and requires a good initial guess or a warm-start strategy, further complicating the development process. Existing methods struggle to integrate the latest hardware designs, such as actuated ankles with closed-loop mechanisms, which offer increased stability, but introduce additional constraints into the dynamics that can be challenging to represent in a computationally tractable fashion. This work introduces a generalized gait optimization framework that directly generates smooth and physically feasible trajectories. The proposed method demonstrates faster and more robust convergence than existing techniques and explicitly incorporates closed-loop constraints. The method is implemented as an open-source C++ codebase that significantly reduces computation times, facilitating dynamic locomotion for full-size humanoids. +

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We present "RAPid and robust Trajectory Optimization toolbox for Robots" (RAPTOR) -- Dynamic locomotion for humanoid robots presents significant analytical and computational challenges due to the extensive number of linkages and degrees of freedom. This complexity results in a vast search space for feasible gaits which translates into a time-consuming process when optimizing over trajectories. In addition, the process often involves numerous hyperparameters and requires a good initial guess or a warm-start strategy, further complicating the development process. Existing methods struggle to integrate the latest hardware designs, such as actuated ankles with closed-loop mechanisms, which offer increased stability, but introduce additional constraints into the dynamics that can be challenging to represent in a computationally tractable fashion. This work introduces a generalized gait optimization framework that directly generates smooth and physically feasible trajectories. The proposed method demonstrates faster and more robust convergence than existing techniques and explicitly incorporates closed-loop constraints. The method is implemented as an open-source C++ codebase that significantly reduces computation times, facilitating dynamic locomotion for full-size humanoids. -

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Paper