InternRobotics/InternNav

Health v2 · maintenance only

Adoption not part of the score

1061 stars · 144 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

InternNav is an open-source PyTorch-based toolbox for building embodied navigation foundation models, supporting vision-language navigation (VLN-CE), visual navigation, and dual-system VLA models like InternVLA-N1. It integrates with Habitat and Isaac Sim simulators and ships datasets, benchmarks, and 10+ baselines for training and evaluating navigation agents.

Use cases

• train a vision-language navigation model
• evaluate VLN agents in Habitat or Isaac Sim
• build a navigation foundation model for robots
• fine-tune InternVLA-N1 on custom navigation data
• benchmark embodied navigation baselines
• collect demonstration datasets for robot navigation
• zero-shot visual navigation in the real world

When to choose

• you need a modular end-to-end navigation research stack with simulators, datasets, and baselines included
• you want to train or evaluate state-of-the-art VLN or dual-system navigation models
• you need compatibility with both Habitat and Isaac Sim

When to avoid

• you need simple point-to-point path planning without learning-based models
• you lack GPU resources for training large VLA models
• you need a production robot navigation stack rather than a research toolbox

Facets

framework · maturity active

machine-learning deep-learning simulation robotics computer-vision nlp llm-training benchmarking data-science robotics machine-learning deep-learning artificial-intelligence simulation autonomous-vehicles python vision-language-navigation vision-language-action-model embodied-ai vln habitat isaac-sim navigation-foundation-model pytorch spatial-intelligence mllm linux gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="InternRobotics/InternNav")