Self-supervised and Transfer Learning for Adaptive Motion Control

自适应运动控制的自监督和迁移学习

• 批准号: RGPIN-2020-04875
• 负责人: Lin, HsiuChin
• 金额: $ 1.75万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750368
• 关键词: Self; supervised; Transfer; Learning; Adaptive

Problem Motion-control in an unstructured environment is one of the hardest unsolved problems in robotics. Robots are skilled in routine and repetitive tasks, but no robot today can easily handle a novel household task without tedious modelling, designing, and programming by a human. Yet, nature solved this challenge half a billion years ago, and for so many human skills, we are not conscious of how a skill is learned or how a task is achieved. While animals handle complex environments with ease, motion-control is still lagging behind nature. Current state-of-the-art In robotics research, model-based control has shown promising results in manipulators and legged robots. Namely, if we have the perfect information of the kinematics (i.e., position, velocity, shape, dimensionality, etc) and dynamics (i.e., mass, center-of-mass, and inertia tensor, etc) of each rigid body in the system, we can find the control torques needed in order to achieve the desired motion. However, this technique is dependent on the accuracy of the kinematics and the dynamic model. While the data-driven approach has been applied to solve non-trivial tasks, this approach requires lots of human labour and suffers from sensory noises. Objectives The long term objective of my research is to develop robust robot control algorithms that are adaptive and versatile in different scenarios. Over the past decade, the newly developed supervised learning approach has been solving many problems that require finding the mapping between some inputs and outputs. In this proposal, we aim to enhance model-based motion control by the newly developed machine learning techniques. An example scenario that we will be targeting is robot grasping problem. Specifically, our short-term objectives are categorized into the following three themes: Theme 1: Estimation of Kinematics and Dynamics Humans are experts in adapting motion into different scenarios. In order for robots to perform well, the controller needs to have the knowledge of the kinematics and the dynamics model of the objects. For this, the first theme focuses on the estimation of kinematics and dynamics without prior knowledge. Theme 2: Self-supervised Learning Kinesthetic teaching, i.e., to manually move the robots and record the motion, requires a large amount of human labour. This theme focuses on the self-supervised learning approach; namely, let the robot samples different scenarios and creates a supervised learning dataset by itself. The effort will be on designing the criterion that increases the information we can gain from the dataset. Theme 3: Simulation-to-real Transfer Learning This theme takes a simulation-to-real transfer learning approach; namely, the models for kinematics and dynamics will be trained (offline) from data collected in simulation deployed on a real robotic platform. By doing so, we can alleviate the problems arisen from sensory noise and ensures the computation can be done in real-time.

非结构化环境中的运动控制是机器人领域最难解决的问题之一。机器人擅长日常和重复性的任务，但今天没有机器人可以轻松地处理一个新的家庭任务，而无需繁琐的建模，设计和编程的人。然而，大自然在5亿年前就解决了这一挑战，对于如此多的人类技能，我们没有意识到一项技能是如何学习的，或者一项任务是如何完成的。虽然动物可以轻松地处理复杂的环境，但运动控制仍然落后于自然。 在机器人研究中，基于模型的控制在机械手和腿式机器人中显示出了良好的效果。也就是说，如果我们有运动学的完美信息（即，位置、速度、形状、维度等）和动态（即，质量、质心和惯性张量等），我们可以找到为了实现期望运动所需的控制力矩。然而，这种技术依赖于运动学和动力学模型的准确性。虽然数据驱动的方法已被应用于解决非平凡的任务，但这种方法需要大量的人力，并受到感官噪音的影响。目标我的研究的长期目标是开发鲁棒的机器人控制算法，在不同的情况下是自适应和通用的。在过去的十年中，新开发的监督学习方法已经解决了许多需要找到一些输入和输出之间的映射的问题。在这个建议中，我们的目标是通过新开发的机器学习技术来增强基于模型的运动控制。我们将针对的一个示例场景是机器人抓取问题。具体来说，我们的短期目标分为以下三个主题：主题1：运动和动力学的估计人类是适应运动到不同场景的专家。为了使机器人表现良好，控制器需要具有对象的运动学和动力学模型的知识。为此，第一个主题集中在没有先验知识的运动学和动力学的估计。主题2：自我监督学习动觉教学，即，手动移动机器人并记录运动需要大量的人力。本主题重点介绍自监督学习方法;即让机器人对不同的场景进行采样，并自行创建监督学习数据集。我们将努力设计一个标准，以增加我们可以从数据集中获得的信息。 主题三：模拟到真实的迁移学习本主题采用模拟到真实的迁移学习方法;即，运动学和动力学模型将根据部署在真实的机器人平台上的模拟中收集的数据进行训练（离线）。通过这样做，我们可以减轻由感官噪声引起的问题，并确保计算可以实时完成。