| Summary: | 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 situations inspired by the Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of the collaborative tasks in order to align with the instantaneous 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 the robot to obtain a smooth composite trajectory planning which crosses expected via points. Decomposition strategy reflects how the desired via point state is projected onto the 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, the robot can be applied to multiple tasks in industrial factories and collaborate with the worker to switch from one task to another according to changing intentions of the human. Classical via points trajectory planning experiments and human-robot collaboration experiments are performed on the Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.
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