A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation
This work addresses contact modeling in robotic manipulation, providing incremental improvements for researchers in trajectory optimization.
The paper tackled the problem of contact-implicit trajectory optimization for manipulation by analyzing three contact models, and found that the proposed variable smooth contact model offered a good trade-off between physical fidelity and motion quality, though with increased computation time.
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.