Physics-based learning for smarter motion controllers

自主平台大多受限于其处理变化或意外情况的能力。这项拟议中的研究旨在通过研究在低级运动控制器中加入学习的方法，使他们更加自主和多才多艺，以便他们的“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挑战类人机器人等）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。通常的体系结构包括反馈法和轨迹生成计划，严重依赖于动态模型。然而，使用这种类型的控制器，机器人不会随着时间的推移而提高他们的技能，并且通常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，并且不适合直接用于机器人移动性问题。正如该领域的主要研究人员Sergey Levine所说：“神经网络在允许我们构建可以处理图像，语音，文本甚至绘制图片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多的新挑战，[...]如果我们能够将大规模机器学习的力量带到机器人控制中，也许我们将更接近解决机器人和自动化的基本问题。* * 拟议的研究旨在开发一种混合控制架构，以利用两个世界的最佳效果（基于模型和学习），这可能是智能运动控制器方面取得突破的关键。更具体地说，短期（5年）目标是：1）提出和评估新的控制架构，包括学习的低级别反馈回路。例如，基于事件的反射反应覆盖基于基线模型的控制器。2)探索使用基于物理的功能来加速学习和普及知识。例如，将适当的机动与无量纲数（摩擦系数、弗劳德数等）相关联。而不是平台特定传感器激励。3)通过实验评估开发的控制算法，使用1/10比例的自动汽车，目标是学习管理不确定和具有挑战性的地形条件。** 最终，拟议的研究旨在开发机器人移动性方面的使能技术，这将有助于在新领域实现自动化，如运输（自动驾驶汽车），采矿，农业，检查，监视甚至家用（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).