Introducing Foresight
Introducing Foresight
Dexterity introduces Foresight, a world model that enables robots to reason about the physical world, predict outcomes, and act confidently in real environments. Trained on over 100 million autonomous actions in production, Foresight supports predictive branching, pragmatic decision-making, capability-aware orchestration, and predictive pipelining. It is designed to be interpretable, safe, and fast, with applications in logistics and industrial automation.
Dexterity introduces Foresight, a world model designed for real-world manipulation tasks, trained on over 100 million autonomous actions in production environments. It emphasizes interpretability, safety, and performance, addressing challenges in spatial reasoning and physical interaction.
Evidence and limits
Published automatically after robotics and source-evidence checks; no manual editorial approval is recorded. Source assertions are not independently verified. Missing information remains not reported.
- Environment:
- Reported productionSource E1
- Control:
- Reported autonomousSource E1
- Data origin:
- Not reported
| Metric | Value / unit | Basis / context | Evidence |
|---|---|---|---|
| autonomous actions | 100,000,000 other | Unique elapsed hours Source wording: “100M+ Autonomous actions in production” | Source E1 |
| placement decision time | 400 other | Basis not reported Source wording: “< 400ms Per placement decision” | Source E1 |
- dataset: Not reported
Source excerpts and review record
No manual editorial approval recorded.
Original source quotation: “Foresight has been trained on experience from over 100 million autonomous actions in production across enterprise logistics operations.”
Source E1
Source：Dexterity — Blog · dexterity.ai