# Foundation Model based Open Vocabulary Task Planning and Executive System for General Purpose Service Robots

arXiv: https://arxiv.org/abs/2308.03357 · HTML: https://mlsanity.com/paper/2308.03357/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

## Built on by (most cited first, 4 of 4)

- [Real-World Robot Applications of Foundation Models: A Review](https://mlsanity.com/paper/2402.05741/) (2024)
- [Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery](https://mlsanity.com/paper/2309.14425/) (2023)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [LiLMaps: Learnable Implicit Language Maps](https://mlsanity.com/paper/2501.03304/) (2025)

## Abstract (arXiv)

This paper describes a strategy for implementing a robotic system capable of performing General Purpose Service Robot (GPSR) tasks in robocup@home. The GPSR task is that a real robot hears a variety of commands in spoken language and executes a task in a daily life environment. To achieve the task, we integrate foundation models based inference system and a state machine task executable. The foundation models plan the task and detect objects with open vocabulary, and a state machine task executable manages each robot's actions. This system works stable, and we took first place in the RoboCup@home Japan Open 2022's GPSR with 130 points, more than 85 points ahead of the other teams.