Smarter robots could soon help with chores in any home

Advancements in robot artificial intelligence could soon bring us closer to the long-promised era of home helper robots. A new AI system enables robots to perform household tasks in unfamiliar environments, potentially solving one of the biggest challenges in robotics: the ability to adapt to new spaces without extensive retraining. This breakthrough could significantly expand the practical applications of robots in everyday settings where conditions constantly change.

The big picture: While large language models have improved robots’ ability to understand verbal commands, most robots still struggle when placed in environments different from their training environments.

• Current robots typically perform well only in spaces they’ve been specifically trained to navigate, severely limiting their real-world usefulness.
• This performance degradation in new environments has been a major obstacle preventing widespread adoption of household robots.

Why this matters: The new AI system enabling adaptability to unfamiliar environments could represent a turning point for practical robotics.

• The ability to perform tasks like making beds or cleaning spills in previously unseen homes would make robots genuinely useful in everyday settings.
• This capability addresses the unpredictable nature of real-world environments, where furniture arrangements, lighting conditions, and obstacle placements constantly change.

What creators are saying: The developers of this technology believe it could finally allow robots to become generally useful in everyday settings.

• They emphasize that the system’s ability to transfer learned skills to new environments is the key factor that distinguishes it from previous approaches.

The technical approach: The system appears to focus on what researchers call “physical intelligence” – the ability to understand and interact with physical environments regardless of specific configurations.

• Unlike traditional robot training that relies heavily on environmental consistency, this approach prioritizes adaptability and generalization of physical tasks.