Incorporating self- and world-models in neural networks for flexible robot learning and control

现有的机器人控制方法在适应新任务方面缺乏灵活性。近年来，基于深度神经网络的交互控制方法--强化学习在解决任务的自学习中显示出良好的应用前景。然而，对于每项新任务，它们都需要与环境进行大量的、通常是随机的交互。相反，人脑学习他们的身体和环境的模型，以有效地预测和计划他们的决定和行动，并可以在线适应。现有的基于模型的控制方案一直受到学习不力的模型的阻碍。该项目将提炼和改进基于认知启发的基于模型的强化学习的各种进展，使机器人能够快速灵活地自学习新任务和适应扰动。我们将学习柔顺机械臂及其环境的多层次模型，然后使用该模型进行规划和控制。这种体系结构将使机器人能够通过在其内部模型上进行更高、人类可解释的级别的规划来自我学习以达到目标状态，并最大限度地减少真实世界的交互，并在线适应。学生将以准确到达和堆叠积木为基准，针对工业使用案例进行构建。

Established methods for robotic control are inflexible in adapting to new tasks. Recently, deep neural network based methods for interactive control, termed reinforcement learning, have shown promise in self-learning to solve tasks. However, they require a huge number of, often random, interactions with the environment for each new task. On the contrary, human brains learn models of their bodies and the environment to efficiently predict and plan their decisions and movements, and can adapt online. Extant model-based schemes for control have been hampered by poorly learned models. This project will distill and improve diverse advances in cognitively-inspired model-based reinforcement learning to enable robots to self-learn new tasks and adapt to perturbations fast and flexibly. We will learn a multi-level model of a compliant robotic arm and its environment, and then use this model for planning and control. This architecture will enable the robot to self-learn to attain goal states, via planning at a higher, human-interpretable level on its internal model with minimal real-world interactions, and also to adapt online. The student will benchmark the architecture on accurate reaching, and stacking blocks, building towards industrial use cases.