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

将自我模型和世界模型结合到神经网络中以实现灵活的机器人学习和控制

• 批准号: 2784464
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2784464
• 关键词: Incorporating; self; world; models; neural

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.

现有的机器人控制方法在适应新任务方面不够灵活。最近，基于深度神经网络的交互式控制方法（称为强化学习）在自我学习解决任务方面表现出了希望。然而，它们需要大量的，通常是随机的，与环境的互动，以完成每一项新任务。相反，人类大脑学习他们的身体和环境模型，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受到学习不足的模型的阻碍。该项目将提取和改进基于认知启发的模型强化学习的各种进展，使机器人能够自学新任务并快速灵活地适应扰动。我们将学习柔顺机械臂及其环境的多层模型，然后使用该模型进行规划和控制。这种架构将使机器人能够通过在其内部模型上以更高的、人类可解释的水平进行规划，以最少的现实世界交互，并在线适应，从而自我学习以达到目标状态。学生将基准的架构准确到达，堆叠块，建设对工业用例。