RI: Small: Probabilistic Goal-Based Imitation Learning

人类非常擅长通过观察和模仿他人来学习新技能。赋予机器人类似能力的尝试未能推广到特定任务之外的领域，部分原因是人们一直把重点放在遵循专家演示的动作轨迹上。目前的项目研究了一种新的跨学科模仿学习方法，这种方法的灵感来自于人类如何通过基于目标的模仿来学习。该项目的具体目标包括：(1)基于推断人类行为的潜在目标而不是遵循轨迹的一种新的模仿方法：根据推断的目标序列执行动作，成功执行的动作序列被缓存为更高级别的目标，从而导致基于目标的分层模仿；(2)提出了一种基于层次贝叶斯模型（HBMs）的新方法，用于跨对象和任务的泛化；(3)提出了基于目标的模仿学习的发展研究，用于测试项目预测？S模型在儿童模仿学习实验中的应用。该项目代表了基于人类学习的见解开发基于目标的模仿学习的严格概率模型的首批努力之一。研究结果有望为新一代机器铺平道路，这些机器可以与人类流畅地互动，从人类老师那里学习新技能，并与人类伙伴合作解决问题。该项目还为研究生和本科生提供计算机科学和认知科学方面的多学科培训，并开展K-12外展活动，鼓励来自代表性不足群体的学生从事科学和工程方面的职业。

Humans are extremely adept at learning new skills by watching and imitating others. Attempts to endow robots with a similar ability have failed to generalize beyond specific tasks, partly because the focus has been on following the trajectory of an action demonstrated by an expert. The current project investigates a new interdisciplinary approach to imitation learning that is inspired by how humans learn via goal-based imitation. The project's specific objectives include: (1) a new method for imitation based on inferring the underlying goals of human actions rather than following trajectories: actions are executed based on sequences of inferred goals and successfully executed action sequences are cached as higher level goals, leading to hierarchical goal-based imitation; (2) a new approach based on hierarchical Bayesian models (HBMs) is proposed for generalization across objects and tasks, and (3) developmental studies of goal-based imitation learning are proposed for testing predictions of the project?s models in imitation learning experiments with children. The project represents one of the first efforts to develop rigorous probabilistic models of goal-based imitation learning based on insights from human learning. The results are expected to pave the way for a new generation of machines that can interact fluently with humans, learn new skills from human teachers, and cooperatively solve problems with human partners. The project also provides graduate and undergraduate students with multidisciplinary training in computer science and cognitive science, with K-12 outreach activities aimed at encouraging students from underrepresented groups to pursue careers in science and engineering.