SURE: Safe Uncertainty-Aware Robot-Environment Interaction using Trajectory Optimization

TL;DR

SURE introduces a trajectory optimization method that explicitly manages contact timing uncertainty, improving robustness and success rates in contact-rich robotic tasks by allowing trajectory branching and rejoining.

Contribution

The paper presents SURE, a novel trajectory optimization framework that handles contact timing uncertainty through trajectory branching, enhancing robustness in contact-rich robotic tasks.

Findings

SURE improves success rate by 21.6% in cart-pole balancing with uncertain wall location.

SURE increases success rate by 40% in robotic egg-catching tasks.

SURE outperforms conventional deterministic trajectory optimization methods.

Abstract

Robotic tasks involving contact interactions pose significant challenges for trajectory optimization due to discontinuous dynamics. Conventional formulations typically assume deterministic contact events, which limit robustness and adaptability in real-world settings. In this work, we propose SURE, a robust trajectory optimization framework that explicitly accounts for contact timing uncertainty. By allowing multiple trajectories to branch from possible pre-impact states and later rejoin a shared trajectory, SURE achieves both robustness and computational efficiency within a unified optimization framework. We evaluate SURE on two representative tasks with unknown impact times. In a cart-pole balancing task involving uncertain wall location, SURE achieves an average improvement of 21.6% in success rate when branch switching is enabled during control. In an egg-catching experiment using a robotic manipulator, SURE improves the success rate by 40%. These results demonstrate that SURE substantially enhances robustness compared to conventional nominal formulations.