CAREER: Robot Reflection in Lifelong Adaptation

终身适应将使机器人能够在无结构和不断演变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，危及它们的任务和周围环境。变化和新颖性是阻碍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对一种名为机器人反思的新范式的基本理解，以应对变化和新颖性，以实现机器人的终身适应。受人类适应认知反思理论的启发，提出的机器人反思范式将允许机器人在不需要人类监督的情况下对自己进行推理并调整自己的适应过程。该奖项将为社会带来重大利益，使能够反思的机器人在其整个生命周期中提高性能，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。为了产生机器人反思终身适应的算法，该奖项的技术目标分为三个方面。第一个推力使机器人能够根据环境和机器人本身的变化组合，通过一种原则性的方法来抵消机器人感知和决策模型的影响，从而使机器人能够在环境中自我感知。第二个推力解决了根据机器人经验中的新颖性程度来适应机器人的问题，通过新颖性估计方法将当前经验与机器人记忆中的过去情节进行比较来估计新颖性，以及机器遗忘方法通过自适应地选择要忘记的过去情节来更新机器人的固定容量记忆，同时避免灾难性遗忘。第三个重点是机器人的想象力适应，而不需要机器人实际体验新的场景，通过一种新的方法，将机器人的想象力表述为顺序决策，并探索可解释的方法来建模经验场景和新场景之间的潜在关系，以实现数据高效的想象。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.