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How can neuroscience be used in robotics ?

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Neuroscience findings offer valuable insights for enhancing robotics capabilities. By integrating principles from neuroscience, robotics can develop unified frameworks for actions in locomotion and manipulation tasks. One approach involves embodying biological neural networks with robot bodies to achieve in vitro biological intelligence, enabling real-time control of robot motion based on neural signals . Furthermore, detailed computational models of the human brain, such as the cerebellum, can guide the development of controllers for robots to mimic human cognitive and motor behaviors effectively. These interdisciplinary efforts not only improve estimation and control algorithms in robotics but also pave the way for addressing challenges like adaptation, robustness, flexibility, generalization, and safe interaction in robotic systems.

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Proceedings Article•DOI A real-time neuro-robot system for robot state control 17 Jul 2022 2 Citations	Neuroscience can be integrated into robotics through real-time neuro-robot systems, where a biological neural network controls a robot's motion state, as demonstrated in the proposed system.
Open access•Journal Article•DOI A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks. Omar Zahra, David Navarro-Alarcon, Silvia Tolu - Show less +2 more 18 May 2021-International Journal of Neural Systems 9 Citations	Neuroscience can be applied in robotics by developing detailed spiking cerebellar models to control robot arms, mimicking human cognitive and motor behavior in manipulation tasks.
Journal Article•DOI Concrete Action Representation Model: From Neuroscience to Robotics John Nassour, Tran Duy Hoa, Payam Atoofi, Fred H. Hamker - Show less +3 more 01 Jun 2020-IEEE Transactions on Cognitive and Developmental Systems 8 Citations	Neuroscience can be applied in robotics by creating a hierarchical action representation model that integrates neural control principles for generating and executing diverse movements efficiently and accurately.
Open access•Posted Content Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception. Pablo Lanillos, Marcel A. J. van Gerven - Show less +1 more 10 May 2021-arXiv: Robotics 2 Citations	Neuroscience-inspired active inference can enhance state estimation, control, and self-perception in robotics, enabling adaptation, robustness, flexibility, generalization, and safe interaction, as discussed in the paper.
Proceedings Article•DOI A real-time neuro-robot system for robot state control Zhe Chen, Tao Sun, Zihou Wei, Xie Chen, Shingo Shimoda, Toshio Fukuda, Qiang Huang, Qing Shi - Show less +7 more 17 Jul 2022 2 Citations	Neuroscience can be integrated into robotics through real-time neuro-robot systems, where a biological neural network controls a robot's motion state based on calcium recording, enabling in vitro biological intelligence.

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What are the uses of neuroscience in robotics ?5 answersNeuroscience plays a crucial role in robotics by providing insights into brain functions that can be applied to enhance robotic systems. It aids in developing neurorobots that mimic brain control mechanisms, enabling a holistic study of neural function and the potential creation of autonomous systems with biological intelligence. Additionally, neuroscience findings contribute to improving estimation and control algorithms in robotics, particularly through active inference models that enhance adaptation, robustness, flexibility, and safe interaction in robots. Furthermore, the hierarchical neural control of movement from cortex to spinal cord inspires models in robotics that facilitate action representation, motor skill transfer, and task performance enhancement. These interdisciplinary connections between neuroscience and robotics drive advancements in both fields, fostering innovation and progress.

What are some important reasons to use reinforcement learning in robotics?4 answersReinforcement learning (RL) is crucial in robotics for several reasons. Firstly, RL allows agents to learn from experience without direct programming, enabling autonomous decision-making. Secondly, RL methods, such as deep RL, have shown potential in enabling robots to acquire complex skills in real-world scenarios. Additionally, the use of RL in controlling multibody systems, combined with Multibody System Dynamics (MSD), offers a systematic approach to developing sophisticated autonomous mechatronic systems. Moreover, RL, when combined with evolutionary algorithms and machine learning control, can solve optimal control problems for objects like quadcopters, ensuring precise movements along desired trajectories. Lastly, RL, when enhanced with gated feature extraction techniques, can significantly improve training speed and policy performance in real-world robot tasks, surpassing traditional algorithms like SAC.

What are some applications of bio robotics?4 answersBio robotics has a wide range of applications. One application is in the field of biomedical signal processing, where signals from living organisms are analyzed to provide useful information for clinicians. Another application is in the development of robots that can function with minimal human interference, with recent advancements focusing on intuitive, designed robots with biomedical functions. Microrobots, constructed using physical, chemical, and biological components, have applications in biomedical and environmental engineering. Bio-inspired robots have been developed to interact with humans and have potential uses in logistics, manufacturing, surgery, and car driving. Biomimetic innovations, such as bio-inspired soft robots and swarm robots, have applications in agriculture, including fruit harvesting, pest control, and crop management. These examples demonstrate the diverse applications of bio robotics in various fields.

How does the computer science applied in robotics?5 answersComputer science is applied in robotics through the integration of projects in robotics into the undergraduate curriculum. This allows students to work on a variety of complex problems and gain hands-on experience with hardware interfaces, low-level input/output, and device drivers. Additionally, computer science is used in the development, design, and programming of robots, which has led to the popularity of robotics as an educational trend. Educational robotics, when integrated into STEAM education, helps cultivate students' soft skills and bridges the gap between theoretical knowledge and practical application. Furthermore, computer science is utilized in the FIRST Robotics Competition, where students design and build robots to compete against other teams. The competition emphasizes the importance of software design and provides students with exposure to computing careers.

How do robotic approaches promote neuroplasticity?3 answersRobotic approaches promote neuroplasticity by providing repetitive, intensive, and task-oriented treatment that facilitates the recovery and rehabilitation of the affected limb. Robot-mediated training amplifies neuroplasticity by enhancing the interaction of proprioceptive and other sensory inputs with motor outputs, leading to significant modifications in functional connectivity within the fronto-parietal networks. Robotic therapy algorithms that amplify movement errors, rather than decrease them, have been proposed to drive motor adaptation and provoke motor plasticity. Studies have shown that robotic therapy can lead to positive changes in central nervous circuits, resulting in improved clinical outcomes. Robotic-exoskeleton training has been found to improve motor outcomes and cortical excitability in patients with stroke, indicating plastic reorganization and use-dependent plasticity. Additionally, brain mapping using magnetic resonance imaging (MRI) in conjunction with robotic devices can serve as a biomarker for brain plasticity induced by rehabilitative motor training.

How is robotic used in neurological surgery?5 answersRobotic systems are used in neurological surgery to assist in executing complicated tasks with high accuracy. These systems have been implemented in various neurosurgical applications such as intracranial biopsies, spinal instrumentation, and placement of intracranial leads. The use of robotics in neurosurgery offers several benefits, including the integration of image guidance with the ability of the robotic arm to reliably execute pre-planned tasks. Robotic surgery provides virtual data, superior spatial resolution, geometric accuracy, dexterity, faster maneuvering, and non-fatigability with steady motion. It also allows for the simulation of virtual procedures, enabling young surgeons to practice their skills in a safe environment and senior professionals to rehearse difficult cases. A worldwide survey among neurosurgeons revealed that almost half of the respondents reported having clinical experience with at least one robotic system, with the highest rates of adoption observed in Europe and North America. However, the progress of robotics development in cranial neurosurgery has been limited by factors such as cost, technology limitations, market size, and regulatory pathways.

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