fabrivia-lightweight-vision-language-action-multi-task-manipulation

name	fabrivia-lightweight-vision-language-action-multi-task-manipulation
description	FabriVLA: lightweight VLA model combining InternVL3.5 VLM backbone with flow-matching action head. Gated self-attention across action tokens, shallow VLM layer fusion. 90.0% success on Meta-World MT50. 1B-scale VLM without billion-parameter backbone. Activation: vision-language-action, lightweight model, multi-task manipulation, flow-matching, robot learning.
metadata	{"arxiv_id":"2607.08575","published":"2026-07-09","authors":"Shiyuan Yang, Borong Zhang, Jizheng Zhang, Zhijia Tao, Junfei Guo","tags":["vision-language-action","lightweight-model","multi-task-manipulation","flow-matching","robot-learning"]}

FabriVLA: A Lightweight Vision-Language-Action Model for Precise Multi-Task Manipulation

Overview

FabriVLA is a lightweight Vision-Language-Action model that demonstrates a compact VLA built on a 1B-scale VLM can achieve strong performance without relying on multi-billion-parameter VLA backbones. It combines an InternVL3.5 vision-language backbone with a flow-matching action head, trained via single-stage joint optimization.

Key Innovations

Compact Architecture

• InternVL3.5 vision-language backbone (1B scale)
• Flow-matching action head with gated self-attention across action tokens
• Shallow VLM layer fusion for enriched spatial context
• No reliance on multi-billion-parameter backbones

Single-Stage Joint Optimization

• Trained from pretrained VLM and randomly initialized action head
• Joint optimization in a single stage
• Avoids complex multi-stage training pipelines

Meta-World MT50 Performance

• 90.0% tier-average success rate across 50 diverse manipulation tasks
• Demonstrates that compact VLA models can match larger architectures
• Strong performance across diverse task types

Methodology

1. Backbone Selection: InternVL3.5 as VLM backbone (1B scale)
2. Action Head Design: Flow-matching head with gated self-attention
3. Fusion Strategy: Shallow VLM layer fusion for spatial context
4. Training: Single-stage joint optimization from pretrained VLM + random action head
5. Evaluation: Meta-World MT50 benchmark (50 tasks)

Implications

• Lightweight VLA models are viable alternatives to large-scale VLA architectures
• 1B-scale VLMs provide sufficient vision-language understanding for manipulation
• Flow-matching action heads with gated attention are effective for action generation
• Single-stage training simplifies VLA deployment

Pitfalls

• Meta-World MT50 is simulation-only — real-world performance not demonstrated
• 1B-scale VLM may struggle with complex language instructions
• Single-stage training may not be optimal for all task domains
• Flow-matching may be slower than direct action prediction at inference

Activation Keywords

vision-language-action, FabriVLA, lightweight VLA, multi-task manipulation, flow-matching, InternVL3.5, robot learning, Meta-World MT50, compact model

Paper Reference

arXiv:2607.08575 - "FabriVLA: A Lightweight Vision-Language-Action Model for Precise Multi-Task Manipulation" (Jul 2026)