FIGURE 1 - available via license: Creative Commons Attribution 4.0 International
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| Graphical User Interface (GUI) of the 3D modeling suite Blender. The Robot Designer is implemented as a plugin to extend the design tools for robot and musculoskeletal modeling, specifically it can be expanded from the menu as shown in the top-right area of the figure.
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The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of the computation required for motion control is offl...
Context in source publication
Context 1
... plugin is integrated into Blenders plugin framework and is regularly updated to newer blender software versions, currently Blender version 2.82. Figure 1 shows the Graphical User Interface of Blender 2.82, on the right you can see the expanded Robot Designer Plugin that is subdivided into multiple tab sessions. ...
Citations
... The model has a movable forelimb composed of three joints and three pairs of antagonistic muscles (i.e., six muscles): Foot1, Radius1, and Humerus 2 as extensors, and Foot2, Radius2, and Humerus1 as flexors. The mouse simulation model including the kinematic configuration and virtual muscles has been configured previously using the NRP Robot Designer (Feldotto et al., 2022b). Each muscle receives real numbers that represents the degree of activation. ...
Embodied simulation with a digital brain model and a realistic musculoskeletal body model provides a means to understand animal behavior and behavioral change. Such simulation can be too large and complex to conduct on a single computer, and so distributed simulation across multiple computers over the Internet is necessary. In this study, we report our joint effort on developing a spiking brain model and a mouse body model, connecting over the Internet, and conducting bidirectional simulation while synchronizing them. Specifically, the brain model consisted of multiple regions including secondary motor cortex, primary motor and somatosensory cortices, basal ganglia, cerebellum and thalamus, whereas the mouse body model, provided by the Neurorobotics Platform of the Human Brain Project, had a movable forelimb with three joints and six antagonistic muscles to act in a virtual environment. Those were simulated in a distributed manner across multiple computers including the supercomputer Fugaku, which is the flagship supercomputer in Japan, while communicating via Robot Operating System (ROS). To incorporate models written in C/C++ in the distributed simulation, we developed a C++ version of the rosbridge library from scratch, which has been released under an open source license. These results provide necessary tools for distributed embodied simulation, and demonstrate its possibility and usefulness toward understanding animal behavior and behavioral change.
... Finally, the proposed work paves the way to the inclusion of large-scale networks in neurorobotic platforms. Indeed, neurorobots platforms simulations such as the one described in [31] adopt LIF models. We proposed a parallel implementation which is not machine dependent, but it is compatible with any CUDA-enabled devices. ...
The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.
... With our work we contribute another aspect to the research question of muscle activation patterns with a similar procedure, but demonstrate alternative muscle control strategies in an isolated knee bending experiment compared to EMG based muscle activation. We integrate the simulated body model in a 3D environment setting that recreates an experiment setup virtually, and the NRP supports the design of such musculoskeletal models and worlds with dedicated design tools (Feldotto et al., 2022). The implementation of spiking neural networks for muscle activation enables a comparison of muscle control paradigms with activation generated by simulated spiking neural network models of the spinal cord in the future. ...
Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well-understood than the cortex. Knowing the contribution of the muscles toward a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible.
Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.
