论文标题
学习机器人动力学的潜在空间以切割交互推理
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
论文作者
论文摘要
在这项工作中探索了对复杂动力学模型的潜在空间的利用来捕获较低维度的表示。针对性的应用程序是一个机器人操纵器，该操作器执行复杂的环境交互任务，尤其是切割木制对象。我们训练两种变异自动编码器的口味---标准和矢量定量 - - 学习潜在空间，然后用来推断切割操作的某些特性，例如机器人是否正在切割，以及对物体的材料和几何形状。通过重建，预测和组合重建/预测解码器评估两个VAE模型。结果表明，机器人相互作用推断的潜在空间和针对复发性神经网络的竞争性预测性能。
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders---standard and Vector-Quantised---to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.
