Abstract -This paper proposes a model that enables a robot to predict and imitate the motions of another by reusing its body forward-inverse model. Our model includes three approaches: (i) projection of a self-forward model for predicting phenomena in the external environment (other individuals), (ii) "triadic relation" that is mediation by a physical object between self and others, (iii) introduction of infant imitation by a parent. The Recurrent Neural Network with Parametric Bias (RNNPB) model is used as the robot's self forward-inverse model. A group of hierarchical neural networks are attached to the RNNPB model as "conversion modules". Experiments demonstrated that a robot with our model could imitate a human's motions by translating the viewpoint. It could also discriminate known/unknown motions appropriately, and associate whole motion dynamics from only one motion snap image.
Another framework with similar goal is also proposed in REF that enables robot to predict and imitate other's motions by translating the viewpoints and utilizing the learned forward-inverse model of the robot's body.
Prediction and imitation of other's motions by reusing own forward-inverse model in robots
2009 IEEE International Conference on Robotics and Automation
2009 IEEE International Conference on Robotics and Automation
Computer Science