RI: Small: Generalizing Learned Manipulation Skills to Unseen Situations by Balancing Uncertainties

对机器人进行编程以执行操作任务需要大量的机器人知识和编程技能。即使对于专家来说，也需要花费大量的时间来精心设计算法和控制器，以便机器人在指定的情况下执行特定的任务。让机器人有能力从人类的演示中学习，将消除编程的必要性及其成本。然而，目前的方法只能复制人类的运动或策略的学习任务在其演示的情况下，但不能将它们转移到看不见的条件。由于在每一种可能的情况下教会机器人所有任务是不现实的，机器人需要能够将在一种情况下学到的操作推广到看不见的情况。该项目的目标是开发一种操作技能泛化方法，该方法考虑到演示中的变化和看不见的情况中的不确定性。通过平衡它们，机器人可以使学习到的操作技能适应新的情况。该项目推进了赋予机器人学习能力并在实践中继续学习的努力。有了这种能力，机器人将能够执行日常生活任务，并为残疾人和老年人提供所需的帮助，而无需编程成本。该项目还将为机器人课程制作新的教材，并培训研究生和本科生。 该项目的主要思想是，机器人应该以更广泛的概率表示形式而不是优化但缩小的拟合形式从演示数据中学习，以便很容易将基于概率的演示学习与看不见的情况下的预测相结合。该项目有四个目标：从演示中学习运动分布推断模型;学习两个操纵估计结果和期望结果的分布;通过合并运动分布和结果分布来生成最优动作;以及在用于日常操纵任务的真实的物理系统上评估该方法。该项目不仅使机器人能够在实践中学习和推广其技能，还为研究和应用提供了新的工具，包括根据经验和可预测的结果做出决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Programming a robot to perform a manipulation task requires a great deal of robotics knowledge and programming skills. Even for an expert, it would take a significant amount of time to carefully craft an algorithm and a controller for a robot to perform a particular task in a specified situation. Giving robots the capability to learn from human demonstrations would remove the need for programming and its cost. However, current approaches can only replicate the human's motion or strategies of the learned task in its demonstrated situations but cannot transfer them to unseen conditions. Since it is unrealistic to teach robots all tasks in every possible situation, robots need to be able to generalize the manipulations kills learned in one situation to unseen situations. The goal of the project is to develop a manipulation skill generalization approach that takes into consideration both the variations in the demonstrations and uncertainties in the unseen situation. By balancing them, the robot can adapt the learned manipulation skills to new situations. The project advances the effort of giving robots the capability to learn and continue their learning in practice. With that capability, robots will be able to perform daily living tasks and provide needed help to people with disabilities and seniors without the cost of programming. The project will also produce new teaching materials for robotics courses and train both graduate and undergraduate students. The main idea of the project is that the robot should learn from the demonstration data in the form of a broader probability representation rather than an optimized but narrowed fitting, so that it is easy to incorporate the probability-based learning from demonstration with the predictions in unseen situations. The project has four objects: learning motion distribution inference model from demonstrations; learning the distributions of two manipulation estimated outcomes and desired outcomes; generating an optimal action by incorporating both motion distributions and outcome distributions; and evaluating the approach on real physical system for daily manipulation tasks. The project not only gives robots a capability to learn and generalize their skills in practice, but also produces new tools for research and applications involving making a decision based on experiences and predictable outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.