Dialogue-driven Multi-Agent Activity Planning
Name
sonar-asonar-sm-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
4.26 MB
Format
Adobe PDF
Checksum (MD5)
ca52bcc41dcb705ffe921881be060401
Author(s)
Sonar, Anoopkumar S.
Advisor(s)
Williams, Brian C.
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
A fundamental challenge in robotics is to build a general-purpose system with multiple agents that can perform a wide range of tasks based on specifications provided in natural language. This work presents a novel dialogue-driven activity planning framework for multiagent scenarios. We present a method that accepts commands from a user in natural language and translates it to an intermediate form called a state plan by leveraging large language models. We further experiment with chain-of-thought prompting to improve the translation from natural language to state plans. In conjunction with an action model, this state plan is utilized by a constraint-based generative planner called ctBurton which outputs a full grounded plan in the form of a state and control trajectory. We demonstrate the utility of our method across three different scenarios– a presentation system, search-and-rescue, and multi-agent assembly– along with experiments on its scalability.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
Persistent DSpace Link
