CAREER: Learning Generalizable and Interpretable Embodied AI with Human Priors

职业：利用人类先验学习可概括和可解释的具体人工智能

• 批准号: 2339769
• 负责人: Bolei Zhou
• 金额: $ 58.66万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339769&HistoricalAwards=false
• 关键词: CAREER; Learning; Generalizable; Interpretable; Embodied

This research aims to leverage human input to facilitate learning generalizable and interpretable AI controllers for autonomous software agents. Specifically, input from people will be incorporated into the learning framework to bias the learning in the form of both offline (before training) and online (during training) feedback. This approach is innovative in terms of three fundamental components of the learning problem agent: the environment, the agent’s representation, and the learning pipeline. The investigator will first develop generative models to learn from images collected from people and to create diverse interactive environments for the learning agent from them. The investigator will then focus on interpreting the internal representation learned by the autonomous agent. Lastly, the investigator will incorporate feedback obtained from online interactions with people to ensure that the resulting AI is safe and aligned with human preferences. The education objective of the project is to connect faculty and students from computer science, engineering, neuroscience, and social sciences by teaching AI as an interdisciplinary subject. The investigator will leverage the project’s interpretability framework to help students understand the AI's inner workings and use the interactive learning methods through simulation environments to create immersive educational experiences and motivate students from diverse backgrounds to learn the underlying mathematical principles between autonomous agents.This research focuses on incorporating humans into three foundational components of learning an embodied agent: the environment, the agent representation, and the learning process: (1) Environment: The investigator will design a machine-learning model to generate and simulate diverse environments from human experiences. It will significantly improve the diversity and complexity of the training environments such that the trained agent can better generalize its acquired skills to unseen situations. (2) Agent representation: The investigator will interpret the learned representation of the embodied agent and discover interpretable motion primitives so that humans can comprehend the AI's internals and control the AI's behaviors in challenging unseen scenarios. (3) Learning process: The investigator will develop reward-free learning and adaptation methods to incorporate active human feedback, substantially improving AI alignment and safety. The investigator will ground the research on indoor and outdoor embodied AI tasks, including autonomous driving, household robot navigation, and legged robot locomotion. This research program seamlessly integrates humans into the machine-learning loop to bring generalizable and interpretable embodied agents to real-world applications.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.

这项研究旨在利用人类的投入来促进为自主软件代理的可推广和可解释的AI控制器学习。具体而言，人们的意见将被纳入学习框架中，以离线（培训之前）和在线（在培训期间）反馈的形式偏向学习。从学习问题代理的三个基本组成部分：环境，代理人的代表和学习管道方面，这种方法具有创新性。研究者将首先开发通用模型，以从从人们那里收集的图像中学习，并为他们的学习代理人创建潜水员的交互式环境。然后，研究人员将专注于解释自主剂学到的内部表示。最后，调查人员将结合与人们在线互动中获得的反馈，以确保由此产生的AI安全并且与人类的偏好保持一致。该项目的教育目标是通过教授AI作为跨学科学科来联系计算机科学，工程，神经科学和社会科学的学生。调查人员将利用项目的可解释性框架来帮助学生了解AI的内部运作，并通过模拟环境使用互动学习方法来创造一个沉浸式的教育经验和来自潜水员背景的成熟学生，以学习自主代理之间的基本数学原理将设计一个机器学习模型，以生成和模拟人类体验中的各种环境。它将显着改善培训环境的多样性和复杂性，以便训练有素的代理可以更好地概括其获得的技能，从而看不见的情况。 （2）代理表示：研究者将解释体现剂的学识渊博的表示，并发现可解释的运动原始词，以便人类可以理解AI的内部内容，并在未见的情况下控制AI的行为。 （3）学习过程：研究人员将开发无奖励的学习和适应方法，以纳入积极的人类反馈，从而大大改善了AI的一致性和安全性。研究人员将基础研究室内和室外体现的AI任务，包括自动驾驶，家用机器人导航和腿部机器人运动。该研究计划将人类无缝整合到机器学习循环中，以将可推广和可解释的体现代理带入现实世界应用。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准通过评估来获得的支持。