Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
This addresses trajectory optimization in robotics for tasks involving contact, offering improved performance for robotic manipulation applications, though it appears incremental as it builds on existing optimization techniques.
The paper tackles contact-implicit trajectory optimization for non-prehensile manipulation by proposing a method based on a variable smooth contact model and successive convexification, which outperforms an iLQR-based method in convergence, computation time, and motion quality, and is validated on a real robot platform.
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, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete 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 SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.