Whether in factory or household scenarios, rhythmic movements play a crucial role in many daily-life tasks. In this paper we propose a Fourier movement primitive (FMP) representation to learn such type of skills from human demonstrations. Our approach takes inspiration from the probabilistic movement primitives (ProMP) framework, and is grounded in signal processing theory through the Fourier transform. It works with minimal preprocessing, as it does not require demonstration alignment nor finding the frequency of demonstrated signals. Additionally, it does not entail the careful choice/parameterization of basis functions, that typically occurs in most forms of movement primitive representations. Indeed, its basis functions are the Fourier series, which can approximate any periodic signal. This makes FMP an excellent choice for tasks that involve a superposition of different frequencies. Finally, FMP shows interesting extrapolation capabilities as the system has the property of smoothly returning back to the demonstrations (e.g. the limit cycle) when faced with a new situation, being safe for real-world robotic tasks. We validate FMP in several experimental cases with real-world data from polishing and 8-shape drawing tasks as well as on a 7-DoF, torque-controlled, Panda robot.

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distributions. However, providing or requesting useful demonstrations is not easy, as quantifying what constitutes a good demonstration in terms of generalization capabilities is not trivial. In this paper, we propose an active learning method for contextual ProMPs for addressing this problem. More specifically, we learn the trajectory distributions using a Bayesian Gaussian mixture model (BGMM) and then leverage the notion of epistemic uncertainties to iteratively choose new context query points for demonstrations. We show that this approach reduces the required number of human demonstrations. We demonstrate the effectiveness of the approach on a pouring task, both in simulation and on a real 7-DoF Franka Emika robot.

We propose organisation conditions that yield a method for training SOM with adaptative neighborhood radius in a variational Bayesian framework. This method is validated on a non-stationary setting and compared in an high-dimensional setting with an other adaptative method.

This article proposes an approach for coupling internally-guided learning and social interaction in the context of a multi-task robot skill acquisition framework. More specifically, we focus on learning a parametrized distribution of robot movement primitives by combining active intrinsically-motivated learning and active imitation learning. We focus on the case where the learning modalities to use are not specified in advance by the experimenter, but are chosen actively by the robot through experiences. Such approach aims at combining experiential and observational learning as efficiently as possible, by relying on a skill acquisition mechanism in which the agent/robot can orchestrate different learning strategies in an iterative manner, and modulate the use of these modalities based on previous experiences. We demonstrate the effectiveness of our approach on a waste throwing task with a simulated 7-DoF Franka Emika robot, where at each iteration of the learning process the robot can actively choose between observational/imitation learning and experiential/intrinsically-motivated learning.