Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
arXiv:1810.10462 · doi:10.1109/ICRA.2019.8794250
Abstract
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.
Accepted for publication in ICRA 2019
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Cited by in corpus (5)
- Contact-Implicit Trajectory Optimization using an Analytically Solvable Contact Model for Locomotion on Variable Ground
- Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories
- Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
- Affordance-Based Mobile Robot Navigation Among Movable Obstacles
- Trajectory Optimization for High-Dimensional Nonlinear Systems under STL Specifications