Skild AI

Publishes technical results for one policy spanning different robots and real-world settings.

No.Paper · what it does and showsIndustry contribution

S1: In-Context Learning for Robotics2026 ↗

A manipulation foundation model built for in-context learning: a single video prompt specifies a task, including unseen tasks over roughly 10-minute horizons, with no weight updates, reaching about 96% accuracy on seen tasks.

Removes per-task data collection and fine-tuning from robot deployment; the company reports S1 is already deployed with commercial partners.

Learning by Watching Human Videos2026 ↗

Finetunes the omni-bodied model on human video — egocentric footage and instructional video — with under an hour of robot data to acquire new skills, tackling the diversity and scale limits of teleoperation.

Frames internet-scale human video as the robot data flywheel, potentially relieving the teleoperation bottleneck that caps how fast the whole industry can add skills.

One Model, Any Scenario: End-to-end Locomotion from Vision2025 ↗

A hierarchical vision-driven policy whose low-level network turns raw images and joint feedback directly into motor commands, so one model walks, climbs stairs and steps over obstacles with no mapping, planning or behaviour switching.

Shows one learned controller can replace terrain-specific locomotion software, which is what lets robot vendors sell a general-purpose machine rather than a site-specific one.