We present a data-driven optimization approach for robotic controlled deposition with a degradable tool. Existing methods make the assumption that the tool tip is not changing or is replaced frequently. Errors can accumulate over time as the tool wears away and this leads to poor performance in the case where the tool degradation is unaccounted for during deposition. In the proposed approach, we utilize visual and force feedback to update the unknown model parameters of our tool-tip. Subsequently, we solve a constrained finite time optimal control problem for tracking a reference deposition profile, where our robot plans with the learned tool degradation dynamics. We focus on a robotic drawing problem as an illustrative example. Using real-world experiments, we show that the error in target vs actual deposition decreases when learned degradation models are used in the control design.

翻译：我们提出了一种基于数据驱动的优化方法，用于配备可退化工具的机器人控制沉积过程。现有方法假设工具尖端不变或频繁更换，而随着工具磨损，误差会随时间累积，在未考虑工具退化因素的沉积场景中导致性能下降。在所提出的方法中，我们利用视觉和力反馈来更新工具尖端的未知模型参数，随后求解一个约束有限时间最优控制问题，以跟踪参考沉积廓形——在此过程中，机器人依据习得的工具退化动力学进行规划。我们以机器人绘图问题作为说明性案例。通过真实世界实验表明，在控制设计中使用习得的退化模型时，目标沉积量与实际沉积量之间的误差显著降低。