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

Artem Bazhenov; Dzmitry Tsetserukou; Vladimir Berman

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.05107 v1 pith:LTGK2CQJ submitted 2024-11-07 cs.RO

Vladimir Berman , Artem Bazhenov , Dzmitry Tsetserukou This is my paper · ORCID

classification cs.RO

keywords approachrobotmobilerobotsmissiontransformeractionsalgorithms

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
cs.RO 2025-01 conditional novelty 5.0 of 10

UAV-VLA generates drone flight plans from natural language using satellite imagery, GPT, and Molmo, and introduces a 30-image benchmark, but its evaluation against a single human operator is weak.

Pith tools