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.

翻译：精确的物体操作与放置是家庭机器人、手术机器人和现场施工机器人面临的常见问题。现有基于计算机视觉、深度传感器和强化学习的方法缺乏对规划误差、执行误差或传感器噪声的主动恢复能力。本研究提出一种利用力-力矩传感的方法，即使在对抗性环境中也能稳健地将物体放置于稳定姿态。在46次试验中，我们的方法在基础堆叠任务中达到了100%的成功率，在需要调整的案例中成功率为17%。