Robot Learning

MT-Opt

MT-Opt is a scalable multi-task reinforcement learning system from Google, introduced in 2021, that trained a single QT-Opt-style Q-learning policy on 12 manipulation tasks using a fleet of seven robots and a shared dataset of hundreds of thousands of real-world episodes. It exploited task interpolation, sharing data across tasks with success-detector relabeling, showing that skills learned jointly outperform independently trained ones and transfer to structurally similar new tasks.

Why it matters for physical AI

Fleet-scale shared experience demonstrated concretely that data reuse across tasks accelerates real-robot reinforcement learning, prefiguring the data-pooling strategies behind today's cross-task and cross-embodiment foundation models.