Green and blue infrastructure as model system for emissions of technology-critical elements.

Over the recent decades, technological advancements have led to a rise in the use of so-called technology-critical elements (TCEs). Environmental monitoring of TCEs forms the base to assess whether this leads to increased anthropogenic release and to public health implications. This study employs an exploratory approach to investigate the distribution of the TCEs Li, Be, V, Ga, Ge, Nb, Sb, Te, Ta, Tl, Bi and the REYs (rare-earth elements including yttrium) in urban aerosol in the city of Vienna, Austria. Leaf samples (n = 292) from 8 plant species and two green facades and water samples (n = 18) from the Wienfluss river were examined using inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). Surface dust contributions were assessed by washing one replicate of each leaf sample and analysing the washing water (n = 146). The impacts of sampling month, plant species and storey level on elemental distribution were assessed by statistical tools and generative deep neural network modelling. Higher TCE levels, including Li, V, Ga, Ge, Tl, Bi, and the REYs, were found in the winter months, likely due to the use of de-icing materials and fossil fuel combustion. A. millefolium and S. heufleriana displayed the highest levels of Li and Ge, respectively. In addition, increased elemental accumulation at lower storeys was observed, including Be, Sb, Bi and the REYs, indicating greater atmospheric dust deposition and recirculation closer to ground level. The results suggest a broad association of TCE levels with urban dust. This study enhances the current understanding of TCE distribution in urban settings and underscores the importance of their inclusion in pollution monitoring. It highlights the complex interplay of human activities, urban infrastructure, and environmental factors, offering valuable insights for managing urban environmental health risks and underlining the need for comprehensive urban ecosystem studies.

Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems.

A salient feature of human motor skill learning is the ability to exploit similarities across related tasks. In biological motor control, it has been hypothesized that muscle synergies, coherent activations of groups of muscles, allow for exploiting shared knowledge. Recent studies have shown that a rich set of complex motor skills can be generated by a combination of a small number of muscle synergies. In robotics, dynamic movement primitives are commonly used for motor skill learning. This machine learning approach implements a stable attractor system that facilitates learning and it can be used in high-dimensional continuous spaces. However, it does not allow for reusing shared knowledge, i.e., for each task an individual set of parameters has to be learned. We propose a novel movement primitive representation that employs parametrized basis functions, which combines the benefits of muscle synergies and dynamic movement primitives. For each task a superposition of synergies modulates a stable attractor system. This approach leads to a compact representation of multiple motor skills and at the same time enables efficient learning in high-dimensional continuous systems. The movement representation supports discrete and rhythmic movements and in particular includes the dynamic movement primitive approach as a special case. We demonstrate the feasibility of the movement representation in three multi-task learning simulated scenarios. First, the characteristics of the proposed representation are illustrated in a point-mass task. Second, in complex humanoid walking experiments, multiple walking patterns with different step heights are learned robustly and efficiently. Finally, in a multi-directional reaching task simulated with a musculoskeletal model of the human arm, we show how the proposed movement primitives can be used to learn appropriate muscle excitation patterns and to generalize effectively to new reaching skills.

Stochastic optimal control methods for investigating the power of morphological computation.

One key idea behind morphological computation is that many difficulties of a control problem can be absorbed by the morphology of a robot. The performance of the controlled system naturally depends on the control architecture and on the morphology of the robot. Because of this strong coupling, most of the impressive applications in morphological computation typically apply minimalistic control architectures. Ideally, adapting the morphology of the plant and optimizing the control law interact so that finally, optimal physical properties of the system and optimal control laws emerge. As a first step toward this vision, we apply optimal control methods for investigating the power of morphological computation. We use a probabilistic optimal control method to acquire control laws, given the current morphology. We show that by changing the morphology of our robot, control problems can be simplified, resulting in optimal controllers with reduced complexity and higher performance. This concept is evaluated on a compliant four-link model of a humanoid robot, which has to keep balance in the presence of external pushes.

Learned graphical models for probabilistic planning provide a new class of movement primitives.

BIOLOGICAL MOVEMENT GENERATION COMBINES THREE INTERESTING ASPECTS: its modular organization in movement primitives (MPs), its characteristics of stochastic optimality under perturbations, and its efficiency in terms of learning. A common approach to motor skill learning is to endow the primitives with dynamical systems. Here, the parameters of the primitive indirectly define the shape of a reference trajectory. We propose an alternative MP representation based on probabilistic inference in learned graphical models with new and interesting properties that complies with salient features of biological movement control. Instead of endowing the primitives with dynamical systems, we propose to endow MPs with an intrinsic probabilistic planning system, integrating the power of stochastic optimal control (SOC) methods within a MP. The parameterization of the primitive is a graphical model that represents the dynamics and intrinsic cost function such that inference in this graphical model yields the control policy. We parameterize the intrinsic cost function using task-relevant features, such as the importance of passing through certain via-points. The system dynamics as well as intrinsic cost function parameters are learned in a reinforcement learning (RL) setting. We evaluate our approach on a complex 4-link balancing task. Our experiments show that our movement representation facilitates learning significantly and leads to better generalization to new task settings without re-learning.