论文标题
机器人食品切割的建模和学习动态
Modelling and Learning Dynamics for Robotic Food-Cutting
论文作者
论文摘要
数据驱动的方法用于建模接触良好的任务解决了分析模型所带来的许多困难。对于实际情况，硬件功能限制了可用的测量结果，因此，问题的每个步骤。在这项工作中，我们提出了一种公式，该公式将基线控制器的知识包裹起来，以实现接触量的食物切割任务。基于此公式，我们采用深层网络来对模型预测控制器中的动态进行建模。我们设计了一个基于课程培训的培训过程，该培训对多步预测的学习率衰减进行了衰减，这对于退缩地平线控制至关重要。实验结果表明，即使有了简单的架构，我们的模型在已知和未知的对象类别上始终如一地实现良好的预测性能，并对长期动态有很好的了解。
Data-driven approaches for modelling contact-rich tasks address many of the difficulties that analytical models bear. For real-world scenarios, the hardware capabilities constrain the available measurements and consequently, every step of the problem's formulation. In this work, we propose a formulation that encapsulates knowledge from a baseline controller for the contact-rich task of food-cutting. Based on this formulation, we employ deep networks to model the dynamics within a model predictive controller. We design a training process based on curriculum training with learning rate decay for multi-step predictions, which are essential for receding horizon control. Experimental results demonstrate that even with a simple architecture, our model achieves consistently good predictive performance on known and unknown object classes and exhibits a good understanding of the long term dynamics.
