Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
Abstract
The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recognition and visual reasoning. Inspired by the dual process theory in cognitive science, which suggests two parallel systems of fast and slow thinking in human decision-making, we introduce Robotics with Fast and Slow Thinking (RFST), a framework that mimics human cognitive architecture to classify tasks and makes decisions on two systems based on instruction types. Our RFST consists of two key components: 1) an instruction discriminator to determine which system should be activated based on the current user instruction, and 2) a slow-thinking system that is comprised of a fine-tuned vision language model aligned with the policy networks, which allows the robot to recognize user intention or perform reasoning tasks. To assess our methodology, we built a dataset featuring real-world trajectories, capturing actions ranging from spontaneous impulses to tasks requiring deliberate contemplation. Our results, both in simulation and real-world scenarios, confirm that our approach adeptly manages intricate tasks that demand intent recognition and reasoning. The project is available at https://jlm-z.github.io/RSFT/
Cite
@article{arxiv.2401.04181,
title = {Language-Conditioned Robotic Manipulation with Fast and Slow Thinking},
author = {Minjie Zhu and Yichen Zhu and Jinming Li and Junjie Wen and Zhiyuan Xu and Zhengping Che and Chaomin Shen and Yaxin Peng and Dong Liu and Feifei Feng and Jian Tang},
journal= {arXiv preprint arXiv:2401.04181},
year = {2024}
}
Comments
accepted to ICRA2024