パラメータ調整不要の接触陰的軌道最適化
Tuning-Free Contact-Implicit Trajectory Optimization
緩和接触モデルと自動ペナルティ調整により、初期推定とチューニングなしで様々なロボットの接触軌道を計画できる手法を提案し、マニピュレーションや歩行で有効性を示した。
著者: Aykut Ozgun Onol, Radu Corcodel, Philip Long, Taskin Padir
分類: cs.RO
原文アブストラクト
We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.
