# Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems

arXiv: https://arxiv.org/abs/2511.02507 · HTML: https://mlsanity.com/paper/2511.02507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Scaling Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.09683/) (2023)
- [SmolVLM: Redefining small and efficient multimodal models](https://mlsanity.com/paper/2504.05299/) (2025)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [World Models for Autonomous Driving: An Initial Survey](https://mlsanity.com/paper/2403.02622/) (2024)
- [FastVLM: Efficient Vision Encoding for Vision Language Models](https://mlsanity.com/paper/2412.13303/) (2024)
- [A Survey of Techniques for Optimizing Transformer Inference](https://mlsanity.com/paper/2307.07982/) (2023)
- [A Review of Multi-Modal Large Language and Vision Models](https://mlsanity.com/paper/2404.01322/) (2024)
- [LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines](https://mlsanity.com/paper/2411.08027/) (2024)
- [Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?](https://mlsanity.com/paper/2405.18361/) (2024)
- [Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark](https://mlsanity.com/paper/2406.01607/) (2024)
- [Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models](https://mlsanity.com/paper/2407.02666/) (2024)
- [Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP](https://mlsanity.com/paper/2404.19071/) (2024)
- [TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models](https://mlsanity.com/paper/2412.15462/) (2024)
- [Small Vision-Language Models: A Survey on Compact Architectures and Techniques](https://mlsanity.com/paper/2503.10665/) (2025)
- [STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models](https://mlsanity.com/paper/2411.00630/) (2024)
- [Vision Language Model-based Testing of Industrial Autonomous Mobile Robots](https://mlsanity.com/paper/2508.02338/) (2025)

## Built on by (most cited first, 1 of 1)

- [Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics](https://mlsanity.com/paper/2609.06629/) (2026)

## Abstract (arXiv)

Recent advancements in Deep Learning enable hardware-based cognitive systems, that is, mechatronic systems in general and robotics in particular with integrated Artificial Intelligence, to interact with dynamic and unstructured environments. While the results are impressive, the application of such systems to critical tasks like autonomous driving as well as service and care robotics necessitate the evaluation of large amount of heterogeneous data. Automated report generation for Mobile Robotics can play a crucial role in facilitating the evaluation and acceptance of such systems in various domains. In this paper, we propose a pipeline for generating automated reports in natural language utilizing various multi-modal sensors that solely relies on local models capable of being deployed on edge computing devices, thus preserving the privacy of all actors involved and eliminating the need for external services. In particular, we evaluate our implementation on a diverse dataset spanning multiple domains including indoor, outdoor and urban environments, providing quantitative as well as qualitative evaluation results. Various generated example reports and other supplementary materials are available via a public repository.