MIT Engineers Teach Robots to Recover from Errors with 'Common Sense'

New method allows household robots to adjust to disruptions and complete tasks without human intervention.

MIT engineers develop a method enabling robots to adjust to disruptions during household tasks, improving their ability to complete tasks without starting over.
The approach connects robot motion data with the 'common sense knowledge' of large language models (LLMs), allowing robots to self-correct execution errors.
Researchers demonstrate the method with a robot trained to scoop and pour marbles, showcasing the robot's ability to recover from being nudged off its path.
The new algorithm learns to identify what subtask a robot is in, facilitating a dialogue between the robot's actions and the LLM's knowledge of subtasks.
This advancement could significantly reduce the need for human programming or intervention when robots encounter errors during tasks.