Abstract

Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals offer a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. We present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. Bayesian algorithms, based on Meltzoff and Moore’s AIM hypothesis for action imitation, implement the core of an imitation learning framework. Our algorithms are computationally efficient, allowing realtime learning and imitation in an active stereo vision robotic head and on a humanoid robot. We present simulated and real-world robotics results demonstrating the viability of our approach. We conclude by advocating a research agenda that promotes interaction between cognitive and robotic studies of imitation.

Citations