Why Fine-Grained Language is the Missing Link in Robot Instruction

Robots following human instructions is nothing new. But what's been lacking is the nuance in those instructions. Vision-Language-Action (VLA) models have been skating by with broad, goal-level language, often leaving out critical execution details. Enter FineVLA, the major shift that’s filling in those blanks.

Filling the Instruction Gap

Picture this: you've got a robot, and it knows the 'what' of the task but not the 'how'. That's where FineVLA steps in. With a reliable framework, FineVLA unifies a staggering 972,247 trajectories from 85,000 tasks across 10 open-source robot datasets. It doesn't stop there. FineVLA-Data, a human-verified dataset, comprises 47,159 fine-grained trajectories. This isn't just numbers. it's precision engineering for robots.

Beyond the Basics

FineVLA doesn't just rest on its laurels. It includes a benchmark of 500 videos, 10,816 atomic facts, and 1,030 VQA questions. That's a heavy arsenal for a robotics-specialized VLM annotator to play with. And yes, it trains a steerable VLA policy with a controlled mix of detailed and goal-level instructions. The results? Fine-grained supervision doesn’t sacrifice the goal. In fact, FG-only approaches improve success rates by +1.4 to +8.1 points.

The Real-World Impact

The mixed instruction approach hits the bullseye at FG:Raw ratios of 1:2 to 1:1, achieving 86.8%/82.5% in RoboTwin simulations and 62.7/100 in real-world dual-arm manipulation. Compare that to the 49.9 of Raw-only instruction, you feel the difference. The fine-grained approach particularly shines in steering control, boosting real-world gains significantly in pose, color, and approach direction. This isn't just theory. it's action.

What’s the Hold Up?

So, why aren't all robots using fine-grained instructions yet? FineVLA's success underscores a big point: vague instructions are yesterday’s news. If robots are to master tasks like humans, they need more than just the 'what'. They need the 'how'. FineVLA is a step in that direction. Are you keeping up?