Learning the Consequences of Actions: Representing Effects as Feature Changes

In advanced Programming by Demonstration (PbD) it is important to give a robot the ability to understand the effects of an action. This ability can enable a robot to not only mimic an action but to imitate, by determining whether an action succeeded or not, or to emulate, by finding another action that causes the same effects as observed. In this paper we propose a system that uses a Bayesian Network structure to store actions as a representation of their effects. The effects in turn are implicitly stored as representation of feature changes in the perceived environment. In a more general form the system can be used to differentiate between actions. In a more specific form it can be used to learn complex mapping functions. We will show three different experiments. The first one shows how to learn actions as a representation of effects. The second one shows how our system can be used to learn a complex mapping function on robot movement and in the third experiment, we illustrate how to combine these independently learned systems to achieve more complex tasks.

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