Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.

Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.