RoboTTT: 8K Context Robot Policies
scott_e_reed · x · 2026-07-15
This research introduces RoboTTT, extending robot policies to an 8K timesteps visuo-motor context. It pushes the usable context length beyond current SOTA levels while maintaining constant inference latency.
Demonstrated capabilities include:
- One-shot imitation from human video demos using only a few minutes of experience
- Continuous improvement during deployment
- Self-recovery from disturbances
- Completion of a 5-minute, 10-step assembly task
Comments emphasize that such TTT methods are highly suitable for robot learning and are likely agnostic to whether the backbone is a VLA or a video model.
Related event: RoboTTT brings test-time training to robot policies(6 posts)→
More from Embodied
- Hugging Face Teases Agentic Training Environments with OpenEnv for August Launch — mervenoyann · 2026-07-22
- NVIDIA shows 22 SIGGRAPH papers and Omniverse tools for robot simulation — facontidavide · 2026-07-22
- Nothing phone mockup turns a film joke into a modular design meme — ZeYanjie · 2026-07-22
- Lightwheel AI Launches SimReadyGen: Text-to-Physics-Accurate Robot Sim Assets — ZeYanjie · 2026-07-22
- Humanoid robot sorting packages in a warehouse sparks debate over job loss — MonaJalal_ · 2026-07-22
- NVIDIA pushes OpenUSD as the common layer for simulation and physical AI — MonaJalal_ · 2026-07-22