# ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

arXiv: https://arxiv.org/abs/2302.07931 · HTML: https://mlsanity.com/paper/2302.07931/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

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

- [Generative AI Agents in Autonomous Machines: A Safety Perspective](https://mlsanity.com/paper/2410.15489/) (2024)
- [PhotoBot: Reference-Guided Interactive Photography via Natural Language](https://mlsanity.com/paper/2401.11061/) (2024)
- [CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots](https://mlsanity.com/paper/2307.11865/) (2023)
- [Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs](https://mlsanity.com/paper/2404.02294/) (2024)

## Abstract (arXiv)

Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.