Physics-based learning for smarter motion controllers

自主平台主要受限于它们处理变化或意外情况的能力。这项拟议的研究旨在通过研究如何在低级运动控制器中加入学习功能，使它们变得更加自主和多才多艺，从而使它们的“运动技能”能够随着时间的推移而提高，并适应新的情况。*最先进的机器人(自动驾驶汽车、DARPA挑战人形机器人等)通常使用基于模型的控制方法，这些方法已经成功地控制了复杂动态系统的运动。通常的体系结构由反馈律和严重依赖动态模型的轨迹生成方案组成。然而，有了这种类型的控制器，机器人的技能并不会随着时间的推移而提高，而且通常会在复杂且难以准确建模的环境中挣扎。另一方面，机器学习算法最近在深度学习方面取得了突破。然而，训练神经网络需要庞大的“问题和答案”数据库，并不直接适用于机器人的移动问题。正如该领域的领军人物谢尔盖·莱文所说：“神经网络在使我们能够构建能够处理图像、语音、文本甚至画图的计算机程序方面取得了长足的进步。然而，引入行动和控制增加了相当大的新挑战，[...]如果我们能将大规模机器学习的力量应用到机器人控制中，也许我们就会离解决机器人学和自动化的基本问题更近一步。*拟议的研究旨在开发一种混合控制体系结构，以利用两个领域的最佳优势(基于模型和学习)，这可能是在更智能的运动控制器方面取得突破的关键。更具体地说，短期(5年)的目标是：1)提出和评估新的控制体系结构，将学习包括在低级别反馈回路中。例如，基于事件的反射反应覆盖了基于基准模型的控制器。2)探索利用基于物理的特征来加速学习和普及知识。例如，将适当的动作与无量纲数(摩擦系数、弗劳德数等)相关联。而不是特定于平台的传感器刺激。3)用1/10比例的自动驾驶汽车对开发的控制算法进行实验评估，目的是学习如何管理不确定和具有挑战性的地形条件。*最终，这项拟议的研究旨在开发机器人移动性方面的使能技术，这些技术将有助于将自动化引入新领域，如交通(自动驾驶汽车)、采矿、农业、检查、监控，甚至家庭使用(自动吸尘器和割草机)。

Autonomous platforms are mostly limited by their ability to handle changing or unexpected situations. The proposed research aims at making them more autonomous and versatile, by investigating ways to include learning in the low-level motion controllers so that their “motor skills” can improve over time and adapt to new situations.******State-of-the-art robots (self-driving cars, DARPA challenge humanoids, etc) typically use model-based control approaches which have been successful for controlling the motion of complex dynamic systems. The usual architecture consist of feedback laws and trajectory generation schemes relying heavily on dynamic models. However, with this type of controllers, robots don't improve their skills over time and usually struggle in environment that are complex and hard to model accurately. On the other hand, machine learning algorithms had a recent breakthrough with deep learning. However, training neural networks require huge databases of “questions and answers”, and is not suitable directly to robotics mobility problems. As stated by Sergey Levine, a leading researcher in the field: "Neural networks have made great strides in allowing us to build computer programs that can process images, speech, text, and even draw pictures. However, introducing actions and control adds considerable new challenges, [...] If we can bring the power of large-scale machine learning to robotic control, perhaps we will come one step closer to solving fundamental problems in robotics and automation."******The proposed research aims at developing an hybrid control architecture to leverage the best of both worlds (model-based and learning), which could be the key for a breakthrough in terms of smarter motion controllers. More specifically, the short-term (5-years) objectives are to: 1) Propose and evaluate novel control architectures to include learning in the low-level feedback loops. For instance, event-based reflex reactions overriding a baseline model-based controller. 2) Explore the use of physics-based features for accelerating the learning and universalizing the knowledge. For instance, correlating appropriate maneuvers to dimensionless numbers (friction coefficient, Froude number, etc.) instead of platform-specific sensor stimulus. 3) Evaluate experimentally the developed control algorithms, with 1/10th scale autonomous cars, with the goal of learning to manage uncertain and challenging terrain conditions. ******Ultimately, the proposed research aims to develop enabling technologies in terms of robotics mobility that will contribute to bring automation in new fields such as transportation (self-driving cars), mining, agriculture, inspection, surveillance and even home use (autonomous vacuum cleaners and lawn mowers).