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

2026-02-06

AI Summary

This work addresses the challenge of insufficient robustness in trajectory optimization for robotic contact-interaction tasks, stemming from uncertainty in contact timing. To this end, the authors propose SURE, a novel framework that explicitly models contact-time uncertainty within trajectory optimization for the first time. SURE introduces a branch-and-merge mechanism: it generates multiple trajectory branches from pre-contact states and subsequently merges them, thereby balancing robustness with computational efficiency. By integrating multi-branch stochastic planning based on trajectory optimization with a real-time branch-switching strategy, SURE significantly outperforms conventional deterministic approaches—achieving success rate improvements of 21.6% in a cart-pole pushing task and 40% in a robotic arm egg-catching task.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• contact interaction
• trajectory optimization
• uncertainty
• robustness
• discontinuous dynamics

Innovation

• trajectory optimization
• contact uncertainty
• robust control
• branching trajectories
• robot-environment interaction