Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Three sources of information in social learning
Imitation in animals and artifacts
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning the affordances of tools using a behavior-grounded approach
Proceedings of the 2006 international conference on Towards affordance-based robot control
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
Visual learning by imitation with motor representations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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To address the problem of estimating the effects of unknown tools, we propose a novel concept of tool representation based on the functional features of the tool. We argue that functional features remain distinctive and invariant across different tools used for performing similar tasks. Such a representation can be used to estimate the effects of unknown tools that share similar functional features. To learn the usages of tools to physically alter the environment, a robot should be able to reason about its capability to act, the representation of available tools, and effect of manipulating tools. To enable a robot to perform such reasoning, we present a novel approach, called Tool Affordances, to learn bi-directional causal relationships between actions, functional features and the effects of tools. A Bayesian network is used to model tool affordances because of its capability to model probabilistic dependencies between data. To evaluate the learnt tool affordances, we conducted an inference test in which a robot inferred suitable functional features to realize certain effects (including novel effects) from the given action. The results show that the generalization of functional features enables the robot to estimate the effects of unknown tools that have similar functional features. We validate the accuracy of estimation by error analysis.