@inproceedings{
sun2023interactive,
title={Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks},
author={Lingfeng Sun and Devesh Jha and Chiori Hori and Siddarth Jain and Radu Corcodel and Xinghao Zhu and Masayoshi Tomizuka and Diego Romeres},
booktitle={NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following},
year={2023},
url={https://openreview.net/forum?id=apEdj9baZx}
}
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. However, planning for these tasks in the presence of uncertainties is challenging as it requires "chain-of-thought" reasoning, aggregating information from the environment, updating state estimates, and generating actions based on the updated state estimates. In this paper, we present an interactive planning technique for partially observable tasks using LLMs. In the proposed method, an LLM is used to collect missing information from the environment using a robot and infer the state of the underlying problem from collected observations while guiding the robot to perform the required actions. We also use a fine-tuned Llama 2 model via self-instruct and compare its performance against a pre-trained LLM like GPT-4. Results are demonstrated on several tasks in simulation as well as real-world environments.
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Lingfeng Sun Devesh K. Jha Chiori Hori Siddarth Jain Radu Corcodel Xinghao Zhu Masayoshi Tomizuka Diego Romeres