论文标题
微型机器人探索的学习：基于模型的运动，稀疏稳定导航和低功率深度分类
Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification
论文作者
论文摘要
在任何规模上构建智能自治系统都是具有挑战性的。微型机器人平台的传感和计算约束使问题更加困难。我们提出了基于学习的方法的改进，用于对微型机器人的运动，分类和导航进行研究。我们展示了如何通过基于模型的强化学习通过板载传感器数据来控制的模拟运动。接下来，我们引入了一个稀疏的线性检测器和动态阈值方法，以快速视觉探光仪，以改善MM量表图像的嘈杂状态。我们以一个新的图像分类器结合了一个新的图像分类器，可以通过结合快速下采样，有效的层结构和硬激活功能来分类。这些是在用力限制的边缘智能和微型机器人世界中使用最先进的算法的有希望的步骤。
Building intelligent autonomous systems at any scale is challenging. The sensing and computation constraints of a microrobot platform make the problems harder. We present improvements to learning-based methods for on-board learning of locomotion, classification, and navigation of microrobots. We show how simulated locomotion can be achieved with model-based reinforcement learning via on-board sensor data distilled into control. Next, we introduce a sparse, linear detector and a Dynamic Thresholding method to FAST Visual Odometry for improved navigation in the noisy regime of mm scale imagery. We end with a new image classifier capable of classification with fewer than one million multiply-and-accumulate (MAC) operations by combining fast downsampling, efficient layer structures and hard activation functions. These are promising steps toward using state-of-the-art algorithms in the power-limited world of edge-intelligence and microrobots.
