Collaborative Research: Inverse Task Planning from Few-Shot Vision Language Demonstrations

协作研究：基于少镜头视觉语言演示的逆向任务规划

• 批准号: 2327973
• 负责人: Sanjiban Choudhury
• 金额: $ 50.73万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327973&HistoricalAwards=false
• 关键词: Collaborative; Research; Inverse; Task; Planning

This project advances national prosperity and welfare by taking a step towards making collaborative robots more accessible across various settings, such as homes, factories, and logistics operations. Today, robots can only be programmed by engineers, limiting the tasks they can do to a narrow set of design choices made by these engineers. For robots to be widely adopted in households, they need to be versatile enough to tackle a much broader range of tasks. Many of these tasks, such as preparing meals or organizing the home, require personalization to the individual user's needs and preferences. This project aims to enable a robot to learn such personalized tasks through natural interactions with the user who may provide visual demonstrations combined with language narration to describe the task. Neither vision nor language alone is perfect, but together, they capture the task that the user wants to convey. This research leverages the broad impact Large Language Model (LLM) interfaces, such as ChatGPT, has had on engaging with everyday users and brings that into the physical realm with robots. It addresses fundamental challenges like summarizing vision-language demonstrations, verifying automatically generated plans and efficiently solving tasks that require many steps to achieve a goal. If successful, the project could transform many consumer robotics applications, enabling robots to be more usable, personalized and aligned with user values.The primary objective of this project is to develop a framework for learning complex, long-horizon tasks from few-shot vision-language demonstrations. While existing approaches in Inverse Reinforcement Learning (IRL) enable learning simple, short-horizon skills from demonstrations, scaling these approaches to longer horizons with fewer demonstrations poses fundamental statistical and computational challenges. To address these challenges, a novel framework called Inverse Task Planning (ITP) that combines the generalization power of Large Language Models (LLMs) with performance guarantees of IRL to both efficiently and verifiably learn tasks will be used. This approach is uniquely different from existing work in LLM and task planning as it creates a closed-loop system to align LLM outputs with human demonstrations. Concretely, the plan is to: (1) parse vision-language demonstrations as robot state-action trajectories using visual question answering (2) learn language-based reward summaries from long-horizon state-action trajectories, and (3) optimize rewards by generating high-level task-code in a verifiable, closed-loop fashion. This research has broad implications for creating new interfaces that allow everyday users to program robots, developing courses on generative models and robotics, and providing immersive and engaging programming activities for K-12 students.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.

该项目通过使协作机器人在家庭、工厂和物流作业等各种环境中更容易获得，从而促进了国家的繁荣和福利。今天，机器人只能由工程师编程，将它们可以完成的任务限制在这些工程师做出的一系列狭窄的设计选择上。为了让机器人在家庭中得到广泛采用，它们需要足够多才多艺，能够处理更广泛的任务。其中许多任务，如做饭或组织家庭，都需要根据个人用户的需求和偏好进行个性化。该项目旨在使机器人能够通过与用户的自然交互来学习这种个性化的任务，用户可以提供结合语言描述的视觉演示来描述任务。无论是视觉还是语言都不是十全十美的，但它们结合在一起，就能捕捉到用户想要传达的任务。这项研究利用了大型语言模型(LLM)接口(如ChatGPT)在与日常用户互动方面的广泛影响，并将其带入了机器人的物理领域。它解决了基本的挑战，如总结视觉语言演示，验证自动生成的计划，以及高效地解决需要许多步骤才能实现目标的任务。如果成功，该项目可能会改变许多消费类机器人应用程序，使机器人变得更有用、更个性化，并与用户价值保持一致。该项目的主要目标是开发一个框架，用于从极少拍摄的视觉语言演示中学习复杂的长期任务。虽然逆强化学习(IRL)中的现有方法可以从演示中学习简单的短期技能，但将这些方法扩展到较长的视野并减少演示会带来基本的统计和计算挑战。为了应对这些挑战，将使用一种名为逆向任务规划(ITP)的新框架，该框架结合了大型语言模型(LLMS)的泛化能力和IRL的性能保证，以有效和可验证地学习任务。这种方法与LLM和任务规划中的现有工作有独特的不同之处，因为它创建了一个闭环系统，使LLM输出与人类演示保持一致。具体地说，该计划是：(1)使用视觉问答将视觉语言演示解析为机器人状态-动作轨迹；(2)从长期状态-动作轨迹中学习基于语言的奖励摘要；(3)通过以可验证的闭环方式生成高级任务代码来优化奖励。这项研究对于创造允许日常用户编程机器人的新界面，开发关于生成模型和机器人的课程，以及为K-12学生提供身临其境和引人入胜的编程活动具有广泛的影响。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。