Brain
Robostral Navigate is Mistral AI's first model for embodied navigation. An 8B parameter model that takes RGB images and natural language instructions to guide robots through complex environments. State-of-the-art on R2R-CE (76.6% success on validation unseen, 79.4% on validation seen). Operates with a single RGB camera, no LiDAR or depth sensors. Built entirely in-house, trained in simulation on ~400K trajectories across 6,000 scenes. Generalizes across wheeled, legged, and flying robots.
Foundation model · Maturity: Research · Closed
Machine-readable surfaces
- Markdown mirror: /brains/robostral-navigate.md
- RSS feed: /brains/robostral-navigate/feed.xml
- JSON-LD: embedded in this page’s head
- REST API: /v1/brains/75e9dffc-773c-4ef7-b0a0-1de2a0f9a850
- Revision history: /brains/robostral-navigate/history
- Data documentation: /data
- Query this programmatically: Deploy MCP
Architecture
8B parameter VLM initialized from vision-language model for grounding (pointing, counting, object localization). Navigation via pointing (infers image coordinates of target location) with fallback to metric displacements. Trained with prefix-caching (tree-based attention masking, 22x token reduction) and online RL (CISPO algorithm, +3.2% improvement).
Key facts
- Type
- Foundation model (embodied navigation)
- Size
- 8B parameters
- Input
- RGB images + natural language instructions
- Sensors required
- Single RGB camera only; no LiDAR or depth sensors
- Benchmark
- State-of-the-art on R2R-CE: 76.6% success on validation unseen, 79.4% on validation seen
- Margin
- Beats best single-camera approach by 9.7 points; beats best multi-sensor by 4.5 points despite no depth sensors
- Training
- Built entirely in-house; trained in simulation on ~400K trajectories across 6,000 scenes; no reliance on existing open-source VLMs
- Efficiency
- Prefix-caching with tree-based attention masking compresses episode into single sequence; 22x token reduction vs per-timestep training
- RL post-training
- Online reinforcement learning (CISPO algorithm) improved success rate by 3.2% after supervised training
- Generalization
- Runs on wheeled, legged, and flying robots; generalizes across robot sizes and camera intrinsics
- Navigation method
- Pointing-based (infers target coordinates in camera view) with fallback to metric displacements when target out of view
- Authors
- Theo Cachet, Arjun Majumdar, Srijan Mishra, Thomas Chabal, Chris Bamford, Elliot Chane-Sane, Benjamin Tibi, Ludovic Ho Fuh, Olivier Duchenne (AI Science Robotics)
Developed by (1)
Common questions
- What is Robostral Navigate?
- Robostral Navigate is Mistral AI's first model for embodied navigation. An 8B parameter model that takes RGB images and natural language instructions to guide robots through complex environments. State-of-the-art on R2R-CE (76.6% success on validation unseen, 79.4% on validation seen). Operates with a single RGB camera, no LiDAR or depth sensors. Built entirely in-house, trained in simulation on ~400K trajectories across 6,000 scenes. Generalizes across wheeled, legged, and flying robots.
- Who developed Robostral Navigate?
- Robostral Navigate is credited to Mistral AI on the DEPLOY registry. Each developer attribution is verified via primary sources.
- Is Robostral Navigate open source?
- No. Robostral Navigate is recorded as proprietary on the DEPLOY registry. Model weights and source are not publicly available.
- What type of AI is Robostral Navigate?
- Robostral Navigate is a foundation model, built on a 8B parameter VLM initialized from vision-language model for grounding (pointing, counting, object localization). Navigation via pointing (infers image coordinates of target location) with fallback to metric displacements. Trained with prefix-caching (tree-based attention masking, 22x token reduction) and online RL (CISPO algorithm, +3.2% improvement). architecture on the DEPLOY registry.
- What is Robostral Navigate's maturity stage?
- Robostral Navigate is at the research stage on the DEPLOY maturity ladder. Research stage means active development without commercial deployments on file.
- Which robots run on Robostral Navigate?
- No robot models on the DEPLOY registry are recorded as running Robostral Navigate. DEPLOY wires brain-to-model connections only when the wiring is verifiable from primary sources; absence may reflect pre-deployment or unverified manufacturer claims.
Sources (3)
Methodology: Unreviewed · 3 sources (no primary) · last reviewed 2026-07-10
Verification posture
Unreviewed
Low confidence
Review state
Stable
Last reviewed 2026-07-10
Maturity + lifecycle
Maturity stage: research
Sources by quality tier
- 3
- unclassified
- Unclassified source
The framework is documented at /methodology. Corrections at /corrections. Reviewer: DEPLOY editorial team.
Methodology surface for Robostral Navigate.Canonical ID 75e9dffc-773c-4ef7-b0a0-1de2a0f9a850