There are countless reasons why home robots have found little success post-Roomba. Pricing, practicality, form factor and mapping have all contributed to failure after failure. Even when some or all of those are addressed, there remains the question of what happens when a system makes an inevitable mistake.

This has been a point of friction on the industrial level, too, but big companies have the resources to address problems as they arise. We can’t, however, expect consumers to learn to program or hire someone who can help any time an issue arrives. Thankfully, this is a great use case for large language models (LLMs) in the robotics space, as exemplified by new research from MIT.