Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention

Abstract

Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situation inspired by Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of collaborative task in order to align with the intantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows robot to obtain a smooth composite trajectory planning which crosses expected viapoints. Decomposition strategy reflects how the desired via-point state is projected onto individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via-points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, robot can be applied to multiple tasks in industrial factories and collaborate with workers to switch from one task to another according to changing intentions of human. Classical viapoints trajectory planning experiments and human-robot collaboration experiment are performed on Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.