让机器人在接触任务中更安全，通过考虑碰撞时间不确定性优化轨迹。
SURE: Safe Uncertainty-Aware Robot-Environment Interaction using Trajectory Optimization
- 用分支再合并的轨迹设计，处理接触时间不确定问题
- 碰撞任务成功率提升21.6%~40%，显著优于传统方法
- 适合需要高鲁棒性的物理交互类机器人应用
涉及接触交互的机器人任务由于动力学不连续，给轨迹优化带来挑战。传统方法通常假设接触事件确定，限制了真实场景中的鲁棒性和适应性。本文提出SURE框架，显式建模接触时机不确定性。通过允许从可能的撞击前状态分叉出多条轨迹，并在后续重新汇合到共同轨迹，SURE在统一优化框架中实现了鲁棒性与计算效率的平衡。我们在两个接触时间未知的任务上评估该方法：在壁面位置不确定的倒立摆平衡任务中，启用分支切换后成功率平均提升21.6%；在机械臂抓蛋实验中，成功率提升40%。结果表明，相比传统基准方法，SURE显著提升了系统鲁棒性。
原文摘要 · Abstract (English)
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.
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