Integrated Machine Learning and Control for Synthesis of Dexterous Manipulation Skills

集成机器学习和控制以综合灵巧的操作技能

• 批准号: RGPIN-2020-04746
• 负责人: Jeon, Soo
• 金额: $ 2.33万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740160
• 关键词: Integrated; Machine; Learning; Control; Synthesis

The rise of collaborative robots promises a new era that robots can coexist with humans in our daily lives, be it home or workplace. Recent strides in machine learning have quickly transformed the way we design autonomous systems even under conditions previously considered too complex to replace humans (e.g. driving a car or playing the game of go). While these data-driven tools offer promises in knowledge-based reasoning such as analyzing image or audio data, it is still a question how such techniques can be transferred to the complex motor control of mechanical devices such as robotic arm and hand that are governed by principles of mechanics. Human extremities are vastly superior to artificial counterparts; this is not in the power and accuracy in executing the pre-programmed task repeatedly, but rather to the richness, adaptability and the sophistication of skilled movements for complex and unpredictable tasks. While we do not yet understand the mechanisms of human sensorimotor coordination, we agree undeniably that human motor skills are mastered through years of practice and learning. The PI has established a leading program focused on the application of advanced sensory data processing and model-based control approaches to enhancing the functionalities of mechanical manipulators. Building on this expertise, the main goal of the proposed research program is to combine recent AI (artificial intelligence) and machine learning techniques with traditional model-based control strategies to endow robotic arms and hands with human-like manipulation skills and adaptability. Specifically, this research aims 1) to develop multi-modal training strategies for semantic perception of various objects and features associated with manipulation tasks, 2) to investigate inverse reinforcement learning (RL) strategies to understand mechanisms behind goal-directed manipulation tasks by human experts, 3) to integrate a priori knowledge on dynamic models of the robot and the environment to enhance the performance of RL, and 4) to develop new simulated training environments as well as a hierarchical RL structure for efficient learning. Successful application of machine learning to physical systems holds enormous potential for breakthroughs in the way we design and operate mechatronic systems by allowing them to seamlessly operate within complex and uncertain environments while constantly interacting with humans and unknown objects. The practical value of the new knowledge and fundamental technologies to be generated through this research program will have a substantial impact on a wide range of industrial and public sectors including Canada's manufacturing, logistics, supply chain, and health care industries. Research tasks associated with this program cut across different disciplines of mechanics, robotics, control theory, signal processing, AI and machine learning, thereby providing ideal training environments to our next generation of highly qualified personnel.

协作式机器人的兴起预示着一个新时代的到来，无论是在家中还是在工作场所，机器人都可以在我们的日常生活中与人类共存。机器学习的最新进展迅速改变了我们设计自主系统的方式，即使在以前被认为过于复杂而无法取代人类的情况下(例如开车或下围棋)。虽然这些数据驱动的工具在基于知识的推理中提供了承诺，如分析图像或音频数据，但如何将此类技术转移到机械设备(如机械臂和手)的复杂电机控制中仍是一个问题，这些设备受机械原理的支配。人类的四肢远远优于人工肢体；这不是因为重复执行预先编程的任务的力量和准确性，而是因为复杂和不可预测的任务所需的熟练动作的丰富性、适应性和复杂性。虽然我们还不了解人类感觉运动协调的机制，但不可否认的是，人类运动技能是通过多年的实践和学习掌握的。PI建立了一个领先的项目，专注于应用先进的感官数据处理和基于模型的控制方法来增强机械机械手的功能。在这些专业知识的基础上，拟议的研究计划的主要目标是将最新的人工智能(AI)和机器学习技术与传统的基于模型的控制策略相结合，赋予机器人手臂和手类似人类的操作技能和适应性。具体地说，本研究的目的是1)开发与操作任务相关的各种对象和特征的语义感知的多模式训练策略；2)研究人类专家理解目标导向操作任务背后的机制的反向强化学习(RL)策略；3)整合关于机器人和环境动态模型的先验知识以提高RL的性能；4)开发新的模拟训练环境以及用于高效学习的分层RL结构。机器学习在物理系统中的成功应用为我们设计和操作机电系统的方式提供了巨大的突破潜力，使它们能够在复杂和不确定的环境中无缝运行，同时不断与人类和未知对象交互。通过这项研究计划产生的新知识和基础技术的实用价值将对包括加拿大制造业、物流、供应链和医疗保健行业在内的广泛的工业和公共部门产生重大影响。与该计划相关的研究任务跨越机械、机器人、控制理论、信号处理、人工智能和机器学习等不同学科，从而为我们的下一代高素质人才提供理想的培训环境。