The researchers address this issue by breaking demonstrations into smaller subsets and using LLMs to label and assign these subactions, eliminating the need for manual programming. In a demonstration, a robot was trained to scoop marbles into a bowl, and when the robot was sabotaged, it was able to self-correct and continue with the task rather than starting over. This method reduces the need for human intervention in programming or correcting robot mistakes.
Key takeaways:
- Home robots have struggled to find success due to issues such as pricing, practicality, form factor, mapping, and the problem of addressing inevitable system mistakes.
- A study from MIT proposes the use of large language models (LLMs) in robotics to help correct mistakes and bring a bit of 'common sense' into the process.
- The research suggests breaking demonstrations into smaller subsets, allowing LLMs to label and assign numerous subactions automatically, enabling a robot to know its stage in a task and recover on its own.
- The study demonstrated this method by training a robot to scoop marbles into a bowl, with the system self-correcting small tasks rather than starting from scratch when it was sabotaged.