NVIDIA: Embodied AI Also Benefits from Context Scaling
dair_ai · x · 2026-07-17
A new NVIDIA paper brings context scaling to embodied AI: foundational robot models shouldn't only look at single steps or short histories, as real-world assembly tasks often last minutes and involve multi-stage decision-making.
The paper introduces RoboTTT, which expands context length by three orders of magnitude over current policies without increasing inference latency. The authors report:
- An overall performance improvement of 87% over single-step baselines on real robot manipulation
- The ability to complete a 5-minute, 10-step assembly task, which baselines failed to finish
- The 8K timestep model outperforming the 1K pre-trained version of the same model
- The first observation that longer pre-training context consistently improves closed-loop performance
The authors emphasize that "context length" itself could be a critical scaling axis in embodied intelligence.
Related event: RoboTTT brings test-time training to robot policies(6 posts)→
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