Abstract
Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, depth sensors, and reinforcement learning lacks the ability to reactively recover from planning errors, execution errors, or sensor noise. This work introduces a method that uses force-torque sensing to robustly place objects in stable poses, even in adversarial environments. On 46 trials, our method finds success rates of 100% for basic stacking, and 17% for cases requiring adjustment.
Abstract (translated)
精确的对象操作和放置是家庭机器人、手术机器人和现场施工机器人中一个常见的问题。之前使用计算机视觉、深度传感器和强化学习的工作,缺乏应对计划错误、执行错误或传感器噪音的反响能力。本文介绍了一种利用力-力矩传感器在稳定姿势中放置物体的方法,即使在具有敌意环境的情况下也是如此。在46次试验中,我们的方法在基本堆叠上的成功率为100%,在需要调整的情况下成功率为17%。
URL
https://arxiv.org/abs/2404.17668
