Test-time scaling for robots works better when you combine reasoning with action planning, track historical context, and use closed-loop feedback—enabling significant performance gains without retraining.
E-TTS is a framework that improves robot manipulation by combining reasoning and action planning at test time, using historical context and feedback loops. It works with existing vision-language-action models without retraining, achieving up to 33% performance gains in simulation and 27% in real-world tasks.