mini-VLA

mini-VLA is a minimal, beginner-friendly Vision-Language-Action (VLA) model designed to show how modern robot policies can fuse images, text instructions, and robot states to produce continuous actions.

Important

it is far from being production-ready! Purpose? Education. Optimizations? Yes, I am developing them. Checkout other branches.

This project intentionally keeps the codebase small (~150 LOC for the core model) so that,

• beginners can understand the complete VLA training and exec pipeline
• researchers can rapidly prototype new ideas around this
• students can learn diffusion-based action generation w/o heavy dependencies

Tip

I recommend reading the following blogs to get started with mini-VLA implementation.

• Building Vision-Language-Action Model from scratch (Basics)
• Building VLA models from scratch — II (Math, Code, and Intuition)
• Upgrading mini-VLA with CLIP/SigLIP vision encoders

This project is not meant to be state-of-the-art instead, it provides a clear, hackable template for understanding VLA design.

The mini-VLA model core is mainly four files: models/encoders.py contains encoders for images, text and states corresponding to the robot, models/fusion.py simply combines vision-language-action embeddings using an MLP (yeah, not ideal but simple and it works OKAY), models/diffusion_head.py generates action using diffusion policy, and models/vla_diffusion_policy.py combines everything!

Additionally, I provide scripts such as scripts/collect_data.py to collect data using an expert policy, scripts/train.py to train the VLA on the collected data, and scripts/test.py to test VLA-Diffusion Policy's performance (+ save videos).

Getting started

Create (or activate) a conda environment

conda create --name mini-vla python=3.10
conda activate mini-vla

Clone mini-VLA project

git clone https://github.com/keivalya/mini-vla.git
cd mini-vla

Install dependencies

pip install -r requirements.txt

Collect demonstration data

This gathers trajectories using an expert Meta-World policy and saves them in .npz dataset.

python -m scripts.collect_data \
  --env-name push-v3 \
  --camera-name corner \
  --episodes 100 \
  --max-steps 100 \
  --output-path data/metaworld_push_bc.npz

Train your VLA model

Train a small vision-language diffusion policy on your collected dataset.

python -m scripts.train \
  --dataset-path data/push_v3.npz \
  --epochs 50 \
  --batch-size 64 \
  --save-path checkpoints/model.pt \
  --device cpu

Test your model in sim

Run the trained VLA inside the Meta-World MT1 environment.

python -m scripts.test \
  --checkpoint checkpoints/model.pt \
  --env-name push-v3 \
  --episodes 5 \
  --max-steps 150 \
  --instruction "push the object to the goal" \
  --device cpu \
  --save-video \
  --video-dir videos

Inference (coming soon)

Planning to,

• support for multiple tasks (MT10 or M50 something, let's see how much I can scale it)
• adding larger vision/text backbones (CLIP, SigLIP, ViT) -- w/o losing simplicity
• arbitrary text-input during inference