CAREER: Robot Reflection in Lifelong Adaptation

终身适应将使机器人能够在非结构化和不断变化的世界中长时间运行。如果没有有效的适应，终身机器人将不可避免地失败，危及其任务和周围环境。变化和新奇是阻止机器人实现终身适应的两个最大挑战。该奖项的目标是开发和获得一种新的范式的基本理解，称为机器人反射，以解决变化和新奇，以实现终身机器人适应。受人类适应的认知反射理论的启发，所提出的机器人反射范式将允许机器人对自己进行推理，并在不需要人类监督的情况下调整自己的适应过程。该奖项旨在通过使具有反思能力的机器人在其生命周期内提高性能，从而广泛影响机器人必须长时间运行的终身机器人应用，如搜索和救援以及自动驾驶，为社会带来重大利益。为了开发终身适应机器人反思的算法，该奖项的技术目标分为三个重点。第一个推力使自我意识的机器人适应的背景下，通过一个原则性的方法，根据环境和机器人本身的变化的组合，抵消机器人的感知和决策模型。第二推力地址适应机器人适应根据在机器人的经验的新奇程度，通过一个新奇估计方法，比较当前的经验对过去的情节在机器人的记忆中，以估计新奇，和机器遗忘的方法，以更新机器人的固定容量的内存，通过自适应地选择过去的情节忘记，同时避免灾难性的遗忘。第三个重点是机器人通过想象力进行适应，而不需要机器人亲身体验新的场景，通过一种新颖的方法，将机器人想象力公式化为顺序决策，并探索可解释的方法，用于对经验和新的数据场景之间的潜在关系进行建模，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进行评估，更广泛的影响审查标准。

Lifelong adaptation will enable robots to operate over long periods of time in unstructured and ever-evolving worlds. Without effective adaptation, lifelong robots will inevitably fail, jeopardizing both their missions and surrounding environments. Change and novelty are two of the greatest challenges that prevent robots from achieving lifelong adaptation. The goal of this award is to develop and gain a fundamental understanding of a novel paradigm, called robot reflection, to address change and novelty in order to achieve lifelong robot adaptation. Inspired by theories of cognitive reflection for human adaptation, the proposed robot reflection paradigm will allow a robot to reason about itself and adjust its own adaptation process without requiring human supervision. This award stands to provide significant benefit to society by enabling robots that reflect to improve performance over their lifetimes, thus broadly impacting lifelong robotics applications in which robots must operate over long periods of time, such as search and rescue and autonomous driving.To produce algorithms for robot reflection in lifelong adaptation, the technical aims of this award are divided into three thrusts. The first thrust enables self-aware robot adaptation in context, through a principled method that offsets a robot perception and decision making model according to combinations of changes in the environment and the robot itself. The second thrust addresses adapting robot adaptation according to the degree of novelty in the robot's experiences, through a novelty estimation approach that compares the current experiences against past episodes in the robot's memory to estimate novelty, and a machine forgetting method to update the robot's fixed-capacity memory by adaptively selecting past episodes to forget while avoiding catastrophic forgetting. The third thrust focuses on robot adaptation by imagination without requiring a robot to physically experience a new scenario, through a novel approach that formulates robot imagination as sequential decision making and explores interpretable methods for modeling the underlying relationships between experienced and new scenarios for data-efficient imagination.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.