FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies

Researchers introduce FineVLA, a framework that enhances Vision-Language-Action models for robotics by incorporating fine-grained instruction supervision beyond simple goal-level commands. The system combines 972,247 trajectories into a curated dataset of 47,159 fine-grained trajectories and demonstrates that mixing fine-grained and coarse instructions improves real-world robot manipulation success rates to 62.7% compared to 49.9% with goal-level instructions alone.

FineVLA addresses a fundamental limitation in current robotic AI systems: while Vision-Language-Action models excel at understanding high-level goals, they lack guidance on execution details that humans naturally communicate. The framework tackles this gap by systematizing fine-grained instruction alignment across diverse robot datasets, creating what researchers call a 'steerable' policy that responds to specific directives about approach angles, contact regions, and tool selection.

The robotics industry has struggled with dataset standardization and instruction granularity. Existing robot datasets typically pair movements with coarse task descriptions like "pick up cup," omitting critical procedural details. FineVLA's consolidation of 10 open-source datasets into a unified benchmark with human verification establishes a new standard for instruction annotation in robotics. The development of a robotics-specialized VLM annotator enables scalable production of fine-grained labels without proportional labor increases.

The experimental results carry significant implications for commercial robotics development. Performance gains of 23 points on pose control and 18 points on color and approach direction specification indicate that fine-grained supervision addresses previously unachievable control dimensions. The inverted-U relationship between fine-grained and raw instruction mixing—peaking at 1:2 ratios—reveals that complementarity, not replacement, drives optimal performance. Real-world dual-arm manipulation reaching 62.7% success represents meaningful progress toward practical deployment.

This work influences the trajectory of embodied AI development. As robotics systems transition from controlled lab environments to real-world deployment, the ability to accept nuanced human guidance becomes economically critical. FineVLA's open framework and public benchmark encourage industry adoption and standardization, potentially accelerating progress in steerable robotic policy learning across manufacturing, logistics, and service sectors.

• →FineVLA combines 972,247 trajectories into a curated dataset of 47,159 fine-grained trajectories with human verification for robotic instruction alignment.
• →Fine-grained instruction supervision improved real-world dual-arm manipulation success rates from 49.9% to 62.7% when mixed optimally with goal-level commands.
• →The optimal instruction mixture follows a consistent inverted-U trend, peaking at fine-grained to raw ratios of 1:2 to 1:1, demonstrating complementarity rather than replacement.
• →Fine-grained supervision showed largest real-world gains on pose control (+23), color (+18), and approach direction (+18)—factors where coarse instructions provide no guidance.
• →The framework includes a robotics-specialized VLM annotator enabling scalable fine-grained annotation across diverse robot datasets without proportional labor increases.