Abstract-Movement prioritization is a common approach to combine controllers of different tasks for redundant robots, where each task is assigned a priority. The priorities of the tasks are often hand-tuned or the result of an optimization, but seldomly learned from data. This paper combines Bayesian task prioritization with probabilistic movement primitives to prioritize full motion sequences that are learned from demonstrations. Probabilistic movement primitives (ProMPs) can encode distributions of movements over full motion sequences and provide control laws to exactly follow these distributions. The probabilistic formulation allows for a natural application of Bayesian task prioritization. We extend the ProMP controllers with an additional feedback component that accounts inaccuracies in following the distribution and allows for a more robust prioritization of primitives. We demonstrate how the task priorities can be obtained from imitation learning and how different primitives can be combined to solve even unseen task-combinations. Due to the prioritization, our approach can efficiently learn a combination of tasks without requiring individual models per task combination. Further, our approach can adapt an existing primitive library by prioritizing additional controllers, for example, for implementing obstacle avoidance. Hence, the need of retraining the whole library is avoided in many cases. We evaluate our approach on reaching movements under constraints with redundant simulated planar robots and two physical robot platforms, the humanoid robot "iCub" and a KUKA LWR robot arm.

Movement Primitives are a well-established\ud paradigm for modular movement representation and\ud generation. They provide a data-driven representation\ud of movements and support generalization to novel situations,\ud temporal modulation, sequencing of primitives\ud and controllers for executing the primitive on physical\ud systems. However, while many MP frameworks exhibit\ud some of these properties, there is a need for a uni-\ud fied framework that implements all of them in a principled\ud way. In this paper, we show that this goal can be\ud achieved by using a probabilistic representation. Our\ud approach models trajectory distributions learned from\ud stochastic movements. Probabilistic operations, such as\ud conditioning can be used to achieve generalization to\ud novel situations or to combine and blend movements in\ud a principled way. We derive a stochastic feedback controller\ud that reproduces the encoded variability of the\ud movement and the coupling of the degrees of freedom\ud of the robot. We evaluate and compare our approach\ud on several simulated and real robot scenarios

The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches are limited to low-dimensional latent spaces, due to the computational complexity of solving a non-convex optimisation problem. We propose finding shortest paths in a finite graph of samples from the aggregate approximate posterior, that can be solved exactly, at greatly reduced runtime, and without a notable loss in quality. Our approach, therefore, is hence applicable to high-dimensional problems, e.g., in the visual domain. We validate our approach empirically on a series of experiments using variational autoencoders applied to image data, including the Chair, FashionMNIST, and human movement data sets.

The presence of vermiform appendix in inguinal hernia is rare and is known as Amyand's hernia. We report an Amyand's hernia, where the appendix was found in a right inguinal hernia in one male cadaver aged ninety two years.

Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning, suffer substantially from the correspondence problem when the actions (i.e., motor commands, torques or forces) of the teacher are not observed or the body of the teacher differs substantially, e.g., in the actuation. To address these drawbacks we propose to learn a robot-specific controller that directly matches robot trajectories with observed ones. We present a novel and robust probabilistic model-based approach for solving a probabilistic trajectory matching problem via policy search. For this purpose, we propose to learn a probabilistic model of the system, which we exploit for mental rehearsal of the current controller by making predictions about future trajectories. These internal simulations allow for learning a controller without permanently interacting with the real system, which results in a reduced overall interaction time. Using long-term predictions from this learned model, we train robot-specific controllers that reproduce the expert's distribution of demonstrations without the need to observe motor commands during the demonstration. The strength of our approach is that it addresses the correspondence problem in a principled way. Our method achieves a higher learning speed than both model-based imitation learning based on dynamics motor primitives and trial-and-error based learning systems with hand-crafted cost functions.We successfully applied our approach to imitating human behavior using a tendon-driven compliant robotic arm. Moreover, we demonstrate the generalization ability of our approach in a multi-task learning set-up.