论文标题
线性模型在物体桩操纵中的视觉远见的令人惊讶的有效性
The Surprising Effectiveness of Linear Models for Visual Foresight in Object Pile Manipulation
论文作者
论文摘要
在本文中，我们解决了使用视觉反馈将一堆小物体推入所需目标集的问题。与常规的单对象操纵管道不同，该管道估计了通过姿势参数参数的系统状态，该系统的潜在物理状态很难从图像中观察到。因此，我们采用直接在图像空间中推理的方法，并获取视觉测量的动力学，以综合视觉反馈策略。我们使用图像空间Lyapunov函数提出了一个简单的控制器，并使用三种不同的模型来评估闭环性能：图像预测的基于深度学习的模型，用于图像到图像对象转换，从将每个像素作为粒子视为粒子的对象中心模型，以及使用动作依赖于动作的线性线性线性线性线性线性线性线性的线性系统。通过模拟和实验的结果，我们表明，对于此任务，线性模型出乎意料地效果很好 - 与我们对相同数量数据训练的深层模型相比，实现了更好的预测错误，下游任务性能和对新环境的概括。我们认为，这些结果在模型中提供了一个有趣的示例，这些模型对于操纵中的基于视觉的反馈最有用，考虑到视觉预测的质量，以及与严格的控制设计和分析的严格方法的兼容性。项目网站：https：//sites.google.com/view/linear-visual-foresight/home
In this paper, we tackle the problem of pushing piles of small objects into a desired target set using visual feedback. Unlike conventional single-object manipulation pipelines, which estimate the state of the system parametrized by pose, the underlying physical state of this system is difficult to observe from images. Thus, we take the approach of reasoning directly in the space of images, and acquire the dynamics of visual measurements in order to synthesize a visual-feedback policy. We present a simple controller using an image-space Lyapunov function, and evaluate the closed-loop performance using three different class of models for image prediction: deep-learning-based models for image-to-image translation, an object-centric model obtained from treating each pixel as a particle, and a switched-linear system where an action-dependent linear map is used. Through results in simulation and experiment, we show that for this task, a linear model works surprisingly well -- achieving better prediction error, downstream task performance, and generalization to new environments than the deep models we trained on the same amount of data. We believe these results provide an interesting example in the spectrum of models that are most useful for vision-based feedback in manipulation, considering both the quality of visual prediction, as well as compatibility with rigorous methods for control design and analysis. Project site: https://sites.google.com/view/linear-visual-foresight/home
