MissionGPT: Mission Planner for Mobile Robot based on Robotics Transformer Model

TL;DR

This paper introduces MissionGPT, a Transformer-based neural network approach enabling mobile robots to execute tasks solely using camera data, potentially reducing reliance on traditional perception tools in logistics environments.

Contribution

The paper presents a novel Transformer and LLM-based mission planner that operates without perception algorithms, demonstrating practical application in warehouse logistics robots.

Findings

Achieved over 50% success rate in basic robot actions.

Eliminates need for markings, LiDARs, beacons for robot orientation.

Scalable approach for various robot types and multi-robot systems.

Abstract

This paper presents a novel approach to building mission planners based on neural networks with Transformer architecture and Large Language Models (LLMs). This approach demonstrates the possibility of setting a task for a mobile robot and its successful execution without the use of perception algorithms, based only on the data coming from the camera. In this work, a success rate of more than 50\% was obtained for one of the basic actions for mobile robots. The proposed approach is of practical importance in the field of warehouse logistics robots, as in the future it may allow to eliminate the use of markings, LiDARs, beacons and other tools for robot orientation in space. In conclusion, this approach can be scaled for any type of robot and for any number of robots.