There's a lot of indications that robotics AI is in a data-starved regime - which means that future models are likely to attain better 0-shot performance, solve more issues in-context, generalize better, require less task-specific training, and be more robust.
But it seems like a degree of "RL in real life" is nigh-inevitable - imitation learning only gets you this far. Kind of like RLVR is nigh-inevitable for high LLM performance on agentic tasks, and for many of the same reasons.
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There's a lot of indications that robotics AI is in a data-starved regime - which means that future models are likely to attain better 0-shot performance, solve more issues in-context, generalize better, require less task-specific training, and be more robust.
But it seems like a degree of "RL in real life" is nigh-inevitable - imitation learning only gets you this far. Kind of like RLVR is nigh-inevitable for high LLM performance on agentic tasks, and for many of the same reasons.