Dialogue-driven Multi-Agent Activity Planning
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 met...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156325 https://orcid.org/0000-0003-0478-0254 |
| Summary: | 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. |
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