A Planning Framework for Robotic Insertion Tasks via Hydroelastic Contact Model

Abstract

Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimental data. However, it is both time-consuming and expensive to conduct real-world experiments repeatedly. On the other hand, while the virtual world enables low cost and fast computations by simulators, there still exists a huge sim-to-real gap due to the inaccurate point contact model. Although finite element analysis might generate accurate results for contact tasks, it is computationally expensive. As such, this study proposes a motion planning framework with bilevel optimization to leverage relatively accurate force information with fast computation time. This framework consists of Dynamic Movement Primitives (DMPs) used to parameterize motion trajectories, Black-Box Optimization (BBO), a derivative-free approach, integrated to improve contact-rich insertion policy with hydroelastic contact model, and simulated variability to account for visual uncertainty in the real world. The accuracy of the simulated model is then validated by comparing our contact results with a benchmark Peg-in-Hole task. Using these integrated DMPs and BBO with hydroelastic contact model, the motion trajectory generated in planning is capable of guiding the robot towards successful insertion with iterative refinement.