SpatialVLA

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SpatialVLA is an AI Agents & Automation tool that provides a spatial-enhanced vision-language-action model for robotics. It is trained on 1.1 Million real robot episodes and accepted at RSS 2025.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SpatialVLA?

SpatialVLA is a cutting-edge, spatial-enhanced vision-language-action model designed for robotics research and development. Trained on an extensive dataset of 1.1 Million real robot episodes, it aims to advance robot learning and performance in complex visual-language-action tasks. The model is built on a purely HuggingFace-based, concise code structure, ensuring efficient performance and easy deployment. It supports both pre-training from scratch using large datasets like OXE and RH20T, and fine-tuning with LoRA for real-world experiments with smaller datasets. SpatialVLA also provides evaluation code for benchmarks like SimplerEnv and offers a Model Zoo with various pre-trained and fine-tuned models.

Best used for

Ideal for professors and researchers who need to develop and evaluate advanced robot control policies, train models on large real-world datasets, and fine-tune for specific tasks. Especially valuable for those working on generalist robot policies and spatial understanding in robotics.

Common actions

train robot models

evaluate robot performance

develop robot policies

research robot learning

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Capabilities

Key features

Spatial-enhanced VLA model
1.1M real robot episodes trained
HuggingFace-based code
Pre-training from scratch
LoRA fine-tuning
SimplerEnv benchmark evaluation
Model Zoo

Target Audience

professor

Integrations

hugging-face

Pricing & Plans

Open Source

Free

FAQs

What are the hardware requirements for running SpatialVLA?

For inference, SpatialVLA requires approximately 8.5GB of GPU memory. For pre-training, it was trained on a cluster of 64 A100 GPUs, and fine-tuning typically uses 4 or 8 A100 GPUs. Users can install tcmalloc for better memory management.

Can I use SpatialVLA with my custom robot dataset?

Yes, you can train SpatialVLA on custom datasets. It is recommended to convert your dataset into the RLDS format. You will then need to modify specific configuration files within the SpatialVLA framework to define your dataset's mixture, observation/action spaces, and transformation functions.

What is the primary advantage of SpatialVLA over other VLA models?

SpatialVLA is a spatial-enhanced vision-language-action model trained on 1.1 Million real robot episodes, achieving state-of-the-art performance. It offers significantly faster inference speed with fewer tokens per action and is built on a concise, purely HuggingFace-based code structure for efficient deployment and performance.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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