OpenVLA vs Octo: Which Robot Learning Model to Choose?
A practical comparison for researchers and builders choosing a vision-language-action (VLA) model.
Both OpenVLA and Octo are open-source vision-language-action models for robot learning. Here's how they compare and when to use each.
Architecture
OpenVLA builds on Prismatic VLM and adds action prediction heads. It supports multiple robot morphologies and action spaces. Octo uses a transformer-based architecture trained on Open X-Embodiment data. Both take images + language and output actions.
Training Data
OpenVLA is trained on Open X-Embodiment and additional datasets. Octo is trained on Open X-Embodiment (RT-X, BridgeData, DROID, etc.). Both benefit from large-scale, diverse robot data. See our Datasets catalog for data sources.
Fine-Tuning
Both support fine-tuning on your robot and task. Typically 50–500 demonstrations can improve performance significantly. OpenVLA offers checkpoints for different robot types. Octo's architecture is flexible for new action spaces.
When to Choose OpenVLA
- You need strong out-of-the-box performance on common manipulation tasks
- Your robot is similar to those in Open X-Embodiment (WidowX, ALOHA, etc.)
- You want a well-documented, actively maintained model
When to Choose Octo
- You're experimenting with novel robot morphologies
- You want maximum flexibility for custom action spaces
- You're building on Open X-Embodiment data directly
Data Collection for Fine-Tuning
Whichever model you choose, you'll likely need task-specific demonstrations. We offer data collection services for imitation learning — teleoperation, learning-ready formatting, and QA. Same-day hardware pickup in Palo Alto for rapid iteration.