Learning by showing: An end-to-end imitation leaning approach for robot action recognition and generation

Abstract

Human action recognition and generation for imitation learning are very important topic of the robot-human interaction research field. In this paper, we present a novel approach for human action recognition and robot action generation based on Kinect motion captured data using Hidden Markov Models (HMMs). The robot recognizes the captured human actions using HMMs, and generates the similar actions by the identical learned HMMs. Different from the traditional robot action generation methods, our system generates the robot action and its parameters only from the HMM which is learned from the recognition phase. In this paper, it is a very important point that the robot can recognize and generate action using an identical HMM, and the robot do not need to record any trajectory data for the action generation using transitional method, such as Dynamic Movement Primitives (DMPs). Since the robot action and its parameters are generated from HMMs, we can adjust the parameters to change the robot action speed. In order to improve the action accuracy, we employ an Augmented Lagrange Multiplier method (ALM) to fine-tune the trajectory of the generated action. So that, the fine-tuned action adjusts its trajectory to accurately reach the target point, and keep the similar style to the original action roughly.