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I feel like the bottleneck is getting access to paired (language, other modality) data though (if your other modality isn't images). i.e. "bolt on generalization" is an intuitively appealing concept, but then it reduces to the hard problem of "how do I learn to ground language to e.g. my robot action space?" I haven't seen a robotics + language paper that actually grapples with the grounding problem / tries to think about how to scale the data collection process for language-conditioned robotics beyond annotating your own dataset as a proof-of-concept. Unlike language modeling / CLIP-type pretraining, it seems (fundamentally?) more difficult to find natural sources of supervision of (language, action). I'd be curious about your thoughts on this!

> When it comes to combining natural language with robots, the obvious take is to use it as an input-output modality for human-robot interaction. The robot would understand human language inputs and potentially converse with the human. But if you accept that “generalization is language”, then language models have a far bigger role to play than just being the “UX layer for robots”.