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Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

Telemanipulation enables people to perform tasks in dangerous environments without exposing them to any risk. This also applies for medical applications. Many infections, such as the SARS-CoV-2 virus, spread over the air and can infect the staff while, e.g., taking samples. This paper proposes a shared control algorithm for a telemanipulation system that enables medical staff to easily perform nasopharyngeal swab samplings from a safe distance while maintaining the safety of the patient. We propose a vision-based virtual fixture approach to guide the operator during the approach towards the nostril. Force feedback and velocity scaling is used to improve dexterity and safety during the sampling. We further prove the stability of the system by introducing an energy tank that ensures passivity at all times. Finally, we test the approach on a real telemanipulation setup and demonstrate the improved usability resulting from the guidance of the shared control.

Sampling-based motion planning algorithms have been continuously developed for more than two decades. Apart from mobile robots, they are also widely used in manipulator motion planning. Hence, these methods play a key role in collaborative and shared workspaces. Despite numerous improvements, their performance can highly vary depending on the chosen parameter setting. The optimal parameters depend on numerous factors such as the start state, the goal state and the complexity of the environment. Practitioners usually choose these values using their experience and tedious trial and error experiments. To address this problem, recent works combine hyperparameter optimization methods with motion planning. They show that tuning the planner's parameters can lead to shorter planning times and lower costs. It is not clear, however, how well such approaches generalize to a diverse set of planning problems that include narrow passages as well as barely cluttered environments. In this work, we analyze optimized planner settings for a large set of diverse planning problems. We then provide insights into the connection between the characteristics of the planning problem and the optimal parameters. As a result, we provide a list of recommended parameters for various use-cases. Our experiments are based on a novel motion planning benchmark for manipulators which we provide at https://mytuc.org/rybj.