cd ../projects

$ cat ~/projects/vlm-robotics.md

Vision-Language Model for Robotics

internal
#VLMs#PyTorch#Hugging Face#Distributed Training#Robotics

Training a VLM for robot understanding — multi-GPU pipeline with Hugging Face models, designed for real-world robotic perception and instruction following.

Built a production Vision-Language Model that enables robots to understand natural language instructions grounded in visual observations. The system integrates pre-trained VLMs from the Hugging Face ecosystem with custom fine-tuning on robotics-specific multimodal data.

// key_highlights

  • ▸Custom VLM architecture built on Hugging Face pre-trained models
  • ▸Multi-GPU training pipeline on A100 clusters with distributed data parallelism
  • ▸Integrated vision + text understanding for robotics-specific tasks
  • ▸Designed dataset preprocessing pipeline for multimodal robot data (images, language, sensor streams)
  • ▸Model evaluation and benchmarking framework for robotic perception tasks

This is proprietary work from my role at Agile Robots SE. Source code is not publicly available, but the write-up above describes the architecture and technical approach.