Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.
Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios. An important question in imitation learning is how to generalize a learned motion to novel situations. The motion generalization depends on a set of features which can be represented as feature vectors spanning a feature space, called query space. The purpose of generalization is to find a mapping from this query space to the motion primitive space (MP space). In this paper, we address the problem of generalization of dynamic movement primitives (DMPs) to new queries by applying locally weighted regression (LWR) with radial basis functions (RBF). Since two DMPs differ only in their non-linear part, we transform the problem of DMP generalization to a regression analysis problem. We introduce a task-oriented regression algorithm with a cost function that takes task constraints into consideration and which relies on model switching to solve the problem of poor DMP generalization when using a single regression model for the entire query space. The evaluation shows that our algorithm outperforms related approaches in the literature in terms of generalization capabilities.