So, Where’s My Robot?

Thoughts on Social Machine Learning

Robot Wants & Needs

What should drive a robot’s behavior? Today, robots are designed to do a particular thing, or designed to learn to do a particular thing. What if, instead, robots were internally motivated to do and learn to do useful new things? Maybe there are ways to computationally model the kinds of motivational systems that humans and animals have to create a flexible framework for the meta-control of an autonomous agent’s behavior.

So, what should your robot “want”? I’ve been doing some digging into what motivates human behavior, and efficiently and effectively drives a learner to good learning experiences. Here’s a working list of useful motivations that we may need computational equivalents for a social robot.

(Some of the books/papers that influence this list: Piaget, Lave, Thoman, Meltzoff)

Novelty: One that we’ve mentioned previously, novelty is a compelling idea for a learning machine. Animal and human learners are incredibly tuned to novelty, and are able to detect and seek out novel events in the world in a safe and efficient way.

Mastery: A corollary to novelty, mastery is another important motivation in human learning. That inherent pleasure in ‘figuring it out.’ In many ways it’s a great balance to novelty, the competing drive to find new things versus understand and master the things you’ve found. This is similar to the explore-exploit tradeoff in Reinforcement Learning, but the novelty-mastery tradeoff is rooted in the future goals and survival of the system rather than the abstract goal of maximizing “reward”.

Like-me: This one is a solely human motivation, though the jury is still out. A ‘like-me’ bias, the propensity and ability to map between others’ actions and self actions, is seen at a very early age. As a child grows older, interacting with adults, they come to understand that the adult is ‘like-me’ and is therefore a source of information about actions and skills. This is said to be an important overall motivation for children’s learning, their desire to be like adults and to participate in the adult world.

Interaction: The inherent ability and desire to engage, communicate, and interact with others is seen from an early age. Children put themselves in a good position to learn new things by being able to recognize and seek proximity to their caregivers. Two month olds can actively engage in communication or turn-taking routines with adults. Studies have shown that infants can start and stop communication with their mother through gesture and gaze, and that it is the infants that control the pace of the turn taking interaction. For a social robot, a critical part of learning effectively in its environment will be quickly developing the ability to recognize who it should interact with and being motivated to try to interact with its ‘caregivers’.

Collaboration/partnership: I have less psychological evidence for this one, but it seems like a good motivation for a social robot. I’m of course not the first to suggest that a robot should be fundamentally motivated to be helpful and collaborative with its human partners. The important part will be how to design a system that successfully balances this motivation with other system goals.

April 27th, 2007 Posted by | Machine Learning, Situated Learning | one comment

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