A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation

TL;DR

This paper compares different contact models in trajectory optimization for manipulation, highlighting the proposed variable smooth contact model's balance of accuracy, motion quality, and computational cost.

Contribution

It introduces a novel variable smooth contact model and evaluates its performance against existing models in manipulation tasks.

Findings

The variable smooth contact model offers a good trade-off between physical accuracy and motion quality.

It increases computation time compared to other models.

The model performs well in simulation for a 7-DOF robot arm pushing task.

Abstract

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1) a contact model that is based on complementarity constraints, (2) a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.