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Several studies have reported methods for capturing ground reaction forces in the field. However, these forces are usually measured indirectly with non-interpretable ``black box'' models. Here we report an interpretable model with a function approximation algorithm. The model uses time series of plantar pressure from instrumented insoles to estimate vertical ground reaction forces. The study included data from 16 persons moving at different speeds on a two-belt treadmill equipped with force plates. The introduced regression model, based on a high-dimensional approximation, using only low-dimensional variable interactions, demonstrated its ability to estimate vertical ground reaction forces. The normalized mean square error for velocities between 3 and \\($\$\)9backslash,backslashhbox kmbackslash,backslashhbox \h\^\-1\\\($\$\)ranged from 10.6 to \\($\$\)24.4\backslash,\backslash\%\\($\$\). The accuracy of the presented approach, which can be used to analyze and interpret the learned model, was comparable to that reported in the literature. Furthermore, the evaluation of the learned model is particularly suitable for embedded and portable systems and, after a one-time calibration measurement, allows permanent and laboratory-independent measurement of vertical ground reaction forces.

Gaze is an important and potent social cue to direct others’ attention towards specific locations. However, in many situations, directional symbols, like arrows, fulfill a similar purpose. Motivated by the overarching question how artificial systems can effectively communicate directional information, we conducted two cueing experiments. In both experiments, participants were asked to identify peripheral targets appearing on the screen and respond to them as quickly as possible by a button press. Prior to the appearance of the target, a cue was presented in the center of the screen. In Experiment 1, cues were either faces or arrows that gazed or pointed in one direction, but were non-predictive of the target location. Consistent with earlier studies, we found a reaction time benefit for the side the arrow or the gaze was directed to. Extending beyond earlier research, we found that this effect was indistinguishable between the vertical and the horizontal axis and between faces and arrows. In Experiment 2, we used 100% “counter-predictive” cues; that is, the target always occurred on the side opposite to the direction of gaze or arrow. With cues without inherent directional meaning (color), we controlled for general learning effects. Despite the close quantitative match between non-predictive gaze and non-predictive arrow cues observed in Experiment 1, the reaction-time benefit for counter-predictive arrows over neutral cues is more robust than the corresponding benefit for counter-predictive gaze. This suggests that–if matched for efficacy towards their inherent direction–gaze cues are harder to override or reinterpret than arrows. This difference can be of practical relevance, for example, when designing cues in the context of human-machine interaction.

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.

Embodied Digital Technologies (EDTs) are increasingly populating private and public spaces. How EDTs are perceived in Hybrid Societies requires prior consideration. However, findings on social perception of EDTs remain inconclusive. We investigated social perception and trustworthiness of robots and telepresence systems (TPS) and aimed at identifying how observers' personality traits were associated with social perception of EDTs. To this end, we conducted two studies (N1\thinspace=\thinspace293, N2\thinspace=\thinspace305). Participants rated five different EDTs in a short video sequence of a space sharing conflict with a human in terms of anthropomorphism, sociability/morality, activity/cooperation, competence, and trustworthiness. The TPS were equipped with a tablet on which a person was visible. We found that the rudimentarily human-like TPS was perceived as more anthropomorphic than the automated guided vehicle, but no differences emerged in terms of other social dimensions. For robots, we found mixed results but overall higher ratings in terms of social dimensions for a human-like robot as opposed to a mechanical one. Trustworthiness was attributed differently to the EDTs only in Study 2, with a preference toward TPS and more human-like robots. In Study 1, we did not find any such differences. Personality traits were associated with attributions of social dimensions in Study 1, however results were not replicable and thus, associations remained ambiguous. With the present studies, we added insights on social perception of robots and provided evidence that social perception of TPS should be taken into consideration before their deployment.

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.

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.