It’s likely that before too long, robots will be in the home to care for older people and help them live independently. To do that, they’ll need to learn how to do all the little jobs that we might be able to do without thinking. Many modern AI systems are trained to perform specific tasks by analysing thousands of annotated images of the action being performed. While these techniques are helping to solve increasingly complex problems, they still focus on very specific tasks and require lots of time and processing power to train.
If a robot is to help take care of people in old age, then the range of problems it will encounter in the home will vary enormously compared to these training situations. During the course of a day, robots might be expected to do everything from making a cup of tea to changing the bedding while holding a conversation. These are all challenging tasks that are more challenging when attempted together. No two homes will be the same, which will mean robots will have to learn fast and adapt to their environment. As anyone sharing a home will appreciate, the objects you need won’t always be found in the same place – robots will need to think on their feet to find them.
One approach is to develop a robot capable of lifelong learning which could store knowledge based on experiences, and work out how to adapt and apply it to new problems. After learning to make a cup of tea, the same skills could be applied to making coffee.
The best learning agent that scientists know of is the human mind, which is capable of learning throughout its life – adapting to complex and ever-changing environments and solving a wide variety of problems on a daily basis. Modelling how humans learn could help develop robots that we can interact with naturally, almost like how we’d interact with another person.
Simulating a child’s development