Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
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In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, SCvx combines the advantages of direct and shooting methods for CITO. For evaluations, non-prehensile manipulation tasks are considered. We compare the proposed method to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The proposed SCvx-based method is also tested on a standard robot platform. The results demonstrate that both methods can find physically-consistent motions that achieve the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed approach is shown to perform efficiently for real-world applications.
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