Detecting the functional similarities between tools using a hierarchical representation of outcomes

The ability to reason about multiple tools and their functional similarities is a prerequisite for intelligent tool use. This paper presents a model which allows a robot to detect the similarity between tools based on the environmental outcomes observed with each tool. To do this, the robot incrementally learns an adaptive hierarchical representation (i.e., a taxonomy) for the types of environmental changes that it can induce and detect with each tool. Using the learned taxonomies, the robot can infer the similarity between different tools based on the types of outcomes they produce. The results show that the robot is able to learn accurate outcome models for six different tools. In addition, the robot was able to detect the similarity between tools using the learned outcome models.