Learning of Tool Affordances for autonomous tool manipulation

We present the concept of Tool Affordances to plan a strategy for target object manipulation by a tool via understanding of bi-directional association between Actions, Tools and Effects. Tool Affordances include the awareness within robot about the different kind of effects it can create in the environment using an action and a tool. Robot learns tool affordances by exploring the environment through its motor actions using different tools and learning their association with observed effects. The strength of our model is the robots ability of prediction and inference given some evidence. To deal with uncertainty, redundancy and irrelevant information Bayesian Network as the probabilistic model is chosen for implementation of our Tool Affordance model. We demonstrate a preliminary experiment where robot uses learnt Tool Affordances to correctly infer the most appropriate novel Action and Tool given the observed effects.