Tuning-Free Contact-Implicit Trajectory Optimization

TL;DR

This paper introduces a contact-implicit trajectory optimization method that requires no parameter tuning, using a relaxed contact model and automatic penalty adjustment to efficiently plan contact interactions across various robot types and tasks.

Contribution

The proposed framework enables tuning-free contact-implicit trajectory optimization by combining a relaxed contact model with an automatic penalty adjustment loop, improving solution quality with minimal computational overhead.

Findings

Effective in simulation for manipulation and locomotion tasks

No parameter tuning needed for different robots and tasks

Provides good performance across diverse applications

Abstract

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.