Skill Learning for Humanoid Robots

目前，类人机器人不像人类那样移动。他们没有足够的技能来执行需要与人类和环境互动的任务。通过刻意练习，人类可以很容易地学习新技能。如果类人机器人能够获得像人类一样的技能，它们将能够帮助满足社会的许多需求。我们认为，人类的灵活性源于人类在表现上的不一致性。这种不一致性使人们能够灵活地学习新技能。如果类人机器人有能力获得像人类一样的技能，我们可以期待类人机器人在各种任务上的表现会有巨大的改善。这项提案是机器人研究人员和运动学习和控制研究人员从运动学角度进行的独特合作，旨在捕捉人类运动学习和控制的这些特征，然后在协调和控制算法中实例化这些特征一个人形机器人。此外，我们还与日本产业技术综合研究所（AIST）的研究人员进行了国际合作，通过HRP-2类人机器人的实验验证了我们的研究成果。在这个项目中，我们将研究人类在学习一些复杂任务时的运动。这些受试者将练习整体技能或练习技能的各个部分。然后，我们将观察受试者如何转移到一个新的运动技能。我们将用网络模型对学习人类进行建模，然后尝试将这些模型放在类人机器人中。 类人机器人是否会表现出与人类相同的学习转移？类人机器人能表现出与人类相同类型的错误吗？这些是我们试图从这项研究中回答的一些问题。这项研究的更广泛的影响包括：（i）对运动技能学习和迁移的系统调查，它们的局限性，以及它们的整合，以产生智能人形机器人;（ii）工程及文科研究人员的研究合作，从不同角度进行资讯科技研究及教育，该项目包括：（i）与日本AIST进行国际研究合作;（ii）开发一个网站，供学生使用OpenHRP软件模拟人形机器人;（iii）研究成果将在专业会议和档案期刊出版物中传播。此外，该项目将对本科和研究生教育产生影响，特别是实验研究和高级设计项目。最后，对于外展影响，人形机器人和模拟工具将是一个很好的工具，教育高中学生的团队合作，并鼓励他们选择工程为他们的高等教育和职业生涯。

Currently humanoid robots do not move like human beings. They are not skillful enough to perform tasks that require interactions with humans and the environment. Humans can learn new skills very easily with deliberate practice. If humanoid robots could acquire skills like humans, they would be able to help with many needs of society.Humans exhibit remarkable flexibility in motor skill. We believe that human flexibility is derived from the fact that humans are inconsistent in performance. This inconsistency allows people the flexibility to learn new skills. Should humanoid robots have the ability to acquire skills like humans, we can expect a dramatic improvement in the performance of humanoid robots on various tasks.This proposal, a unique collaboration between a robotics researcher and a motor learning and control researcher from a kinesiology perspective, aims at capturing these characteristics of human motor learning and control and then instantiating these characteristics in the coordination and control algorithms of a humanoid robot. In addition, an international collaboration with researchers at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan will allow us to examine the goodness of our research experimentally on an HRP-2 humanoid robot.In this project, we will be studying the motion of human subjects as they learn a few complicated tasks. These subjects will either practice the skill as a whole or practice the individual parts of the skill. We then will observe how well subjects transfer to a novel motor skill. We will model the learning human with network models and then attempt to put these models in a humanoid robot. Will the humanoid robot show the same transfer of learning as the human? Can the humanoid robot show the same types of errors as the human? These are some of the questions that we seek to answer from this research.The broader impacts of this research include: (i) a systematic investigation of motor skill learning and transfer, their limitations, and their integration to produce intelligent humanoid robots; (ii) research collaboration between researchers from engineering and liberal arts to engage in information technology research and education from different perspectives, and international research collaboration with the AIST in Japan; (iii) development of a web site for students to simulate humanoid robots using OpenHRP software; and (iv) research results will be disseminated in professional conference and archival journal publications. In addition, the project will have an impact on undergraduate and graduate education, especially experimental research and senior design projects. Finally, for the outreach impact, humanoid robots and simulation tools will be an excellent vehicle to educate high-school students in team work and encourage them to select engineering for their higher education and career.