LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

arXiv:2608.30935 · cs.RO, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-08-31

Updated: 2026-09-09

Project page: https://internrobotics.github.io/internvla-n1.github.io

Terminology

• Benchmark — 34× in this paper · explained in 2 episodes
• Affordance — 29× in this paper · explained in 1 episode
• Grounding — 18× in this paper · explained in 3 episodes
• Fine-Tuning — 14× in this paper · explained in 5 episodes
• Trace — 12× in this paper · explained in 2 episodes
• Generalization — 10× in this paper · explained in 13 episodes
• Foundation Model — 9× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Decode — 5× in this paper · explained in 2 episodes
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Action Space — 3× in this paper · explained in 1 episode
• Foundation Models — 3× in this paper · explained in 7 episodes
• GRPO — 3× in this paper · explained in 6 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Zero-Shot Generalization — 3× in this paper · explained in 5 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Online Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Active Exploration — 1× in this paper · explained in 2 episodes
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Group Relative Policy Optimization — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• 1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
• On Evaluation of Embodied Navigation Agents
• Qwen3-VL Technical Report
• ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Robostral Navigate
• Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation
• $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• PointArena: Probing Multimodal Grounding Through Language-Guided Pointing
• ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
• Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
• EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models
• MolmoAct2: Action Reasoning Models for Real-world Deployment
• Video-R1: Reinforcing Video Reasoning in MLLMs
• OctoNav: Towards Generalist Embodied Navigation
• Gemini Robotics: Bringing AI into the Physical World
• ABot-N1: Toward a General Visual Language Navigation Foundation Model

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