The REAL-RL project aims to make robots smarter and more helpful in our everyday lives by teaching them to learn from their own experiences, much like humans do. Today’s robots are usually designed to do only one specific job, and making them work properly often requires very detailed knowledge about exactly how they should interact with the world around them. This makes it hard – or sometimes impossible – to use them for new or unexpected tasks.
REAL-RL takes a new approach that focuses on helping robots figure things out for themselves by trial and error. Most current methods in this area require robots to practice a huge number of times, which takes too long and can wear out real robots. As a shortcut, scientists use computer simulations, but these still need a lot of knowledge about every possible situation the robot might run into.
Our project is different: we want robots to learn their own "common sense" about how the world works, building up knowledge from their own experiences. With this knowledge, robots can quickly plan what to do and adapt when something unexpected happens—without needing tons of practice or super-detailed instructions.
We are tackling important scientific and technical challenges that stand in the way of this vision, including:
- How robots can build useful models of the world from experience.
- How they can safely explore and learn without making mistakes that could cause damage.
- How they can think ahead and make plans that take into account risks and different possible futures.
We’ve already made progress on some of the building blocks needed to make this happen, like computer programs that can guess what might happen next, or fast ways for robots to plan their actions.
Ultimately, if successful, this project will help build a new generation of robots that can use what they’ve learned to help humans with a wider range of tasks—from walking and carrying, to assembling items, to assisting around the house or workplace. These robots will not just follow instructions, but actually get better with practice, making them much more useful and adaptable in the future.
