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

arXiv:2605.27284 · cs.RO, cs.AI · Submitted 2026-05-26 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-05-26

Updated: 2026-09-02

Code: https://github.com/NVIDIA/Isaac-GR00T

Project page: https://finevla.xlang.ai

Terminology

• Benchmark — 66× in this paper · explained in 2 episodes
• Actor — 26× in this paper · explained in 3 episodes
• Alignment — 22× in this paper · explained in 1 episode
• Clustering — 22× in this paper · explained in 2 episodes
• Hallucination — 20× in this paper · explained in 4 episodes
• Fine-Tuning — 19× in this paper · explained in 5 episodes
• Grounding — 19× in this paper · explained in 3 episodes
• Consistency — 17× in this paper · explained in 3 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Verification — 9× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Human-in-the-Loop — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 3× in this paper · explained in 2 episodes
• Compositional Generalization — 2× in this paper · explained in 4 episodes
• Dynamic Time Warping — 2× in this paper
• Dynamic Time Warping (DTW) — 2× in this paper · explained in 2 episodes
• Hallucination Detection — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Vision-language models — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Decoder — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Alignment Problem — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
• A Pragmatic VLA Foundation Model
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models
• RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
• RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
• HanDyVQA: A Video QA Benchmark for Fine-Grained Hand-Object Interaction Dynamics
• BridgeData V2: A Dataset for Robot Learning at Scale
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
• RT-1: Robotics Transformer for Real-World Control at Scale
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
• RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation
• RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
• RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Octo: An Open-Source Generalist Robot Policy

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