Recent advancements in artificial hand development have primarily concentrated on enhancing adaptive grasping, dexterity, as well as the integration of biomimetic skin. However, few designs have successfully combined ...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Recent advancements in artificial hand development have primarily concentrated on enhancing adaptive grasping, dexterity, as well as the integration of biomimetic skin. However, few designs have successfully combined lightweight, cost-effective solutions, and tactile sensing along with adaptive grasping in a human-sized prototype. We propose, an open-source, highly integrated artificial hand. It leverages a compliant linkage mechanism for versatile grasping capabilities, featuring six degrees of actuation and MEMS-based tactile sensors on every fingertip.
In recent years, the rapid increase in urban vehicles has made finding vacant parking spaces a significant challenge. Our research focuses on reverse parking maneuvers that align with driver habits and enhance safety....
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Nowadays, STEM (Science, Technology, Engineering, and Mathematics) education has been increasingly important. In some developed countries, STEM education plays a role in science integration with other fields of learni...
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This work investigates the integration of complementary FET (CFET) transistors within static random-access memory (SRAM) to deliver aggressive bitcell area scaling and substantial performance gains for deeply scaled C...
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ISBN:
(数字)9798331504168
ISBN:
(纸本)9798331504175
This work investigates the integration of complementary FET (CFET) transistors within static random-access memory (SRAM) to deliver aggressive bitcell area scaling and substantial performance gains for deeply scaled CMOS nodes beyond 3nm. By vertically stacking the pFET atop the nFET, CFETs achieve a profound reduction in the cell area, presenting a viable pathway to meet extreme density demands. Utilizing the industry-standard BSIM-CMG model, we carefully calibrate the CFET device's electrical characteristics against measurements from fabricated devices. Our calibrated model is then applied to a 6T-SRAM cell and critical peripheral circuits, including pre-charge, sense amplifier, and latch configurations. Comprehensive SPICE simulations enable a detailed assessment of SRAM performance, quantifying noise margins, access delays, and power dissipation across both read and write cycles. Our analysis further dissects the delay and power contributions along the signal path, underscoring CFET's transformative potential in advancing SRAM scalability and efficiency in leading-edge technology nodes.
Reaching human-level performance in tactile manipulation is one of the grand challenges in nowadays robotics research. Over the past decade significant progress in both skill control and learning was made. However, th...
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ISBN:
(数字)9781665479271
ISBN:
(纸本)9781665479271
Reaching human-level performance in tactile manipulation is one of the grand challenges in nowadays robotics research. Over the past decade significant progress in both skill control and learning was made. However, the achievable execution speed still falls behind the human ability, without clearly understanding whether the specific shortcomings are mainly in the control, skill learning, or motion planning layer. For gaining a better understanding of this complex problem, we draw an experimental side-by-side comparative case study. First, given a task program for a challenging benchmarking task, the goal is to objectify the achievable task performance from a human expert programmer against autonomously learning these assembly behaviors with a state-of-the-art skill learning framework. Second, we compare the manually tuned and learned robot skills to the performance of an adult human solving the task manually. For the former, it could be shown that despite longer learning duration, the task execution speed of the machine learning-based solution is equivalent to the one programmed by the human expert. For the latter, the identified performance gap remained significantly larger, where only for some specific isolated skills the system was able to reach comparable or even faster than human execution speeds. The overall analysis gave also useful hints where in particular manipulation policies and arm-hand coordination still need significant improvements in the future.
PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One...
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PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network *** developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).ResultsThe results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical *** work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.
The medial entorhinal cortex of rodents is known to contain grid cells that exhibit precise periodic firing patterns based on the animal’s position,resulting in a distinct hexagonal pattern in *** cells have been ext...
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The medial entorhinal cortex of rodents is known to contain grid cells that exhibit precise periodic firing patterns based on the animal’s position,resulting in a distinct hexagonal pattern in *** cells have been extensively studied due to their potential to unveil the navigational computations that occur within the mammalian brain and interesting phenomena such as so-called grid cell distortions have been *** neuronal models of grid cells assumed their firing fields were independent of environmental ***,more recent research has revealed that the grid pattern is,in fact,dependent on the environment’s *** rodents are placed in nonsquare cages,the hexagonal pattern tends to become disrupted and adopts different *** believe that these grid cell distortions can provide insights into the underlying neural circuitry involved in grid cell *** this end,a calibration circuit for grid cells is *** simulations demonstrate that this circuit is capable of reproducing grid distortions observed in several previous *** model also reproduces distortions in place cells and incorporates experimentally observed distortions of speed cells,which present further opportunities for *** generates several experimentally testable predictions,including an alternative behavioral description of boundary vector cells that predicts behaviors in nonsquare environments different from the current model of boundary vector *** summary,our study proposes a calibration circuit that reproduces observed grid distortions and generates experimentally testable predictions,aiming to provide insights into the neural mechanisms governing spatial computations in mammals.
Artificial intelligence is currently achieving impressive success in all ***,autonomous navigation remains a major challenge for *** learning is used for target navigation to simulate the interaction between the brain...
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Artificial intelligence is currently achieving impressive success in all ***,autonomous navigation remains a major challenge for *** learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and *** hippocampus–striatum circuits are considered as key circuits for target navigation planning and *** paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the *** ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward *** this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous *** with traditional methods,this system reflects the high efficiency of learning and better Environmental *** research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.
Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multi-objective Bayesian Optimization (BO) technique...
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ISBN:
(数字)9798350348811
ISBN:
(纸本)9798350348828
Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multi-objective Bayesian Optimization (BO) techniques solve this problem by determining a Pareto optimal parameter set for an MPC with static weights. However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime. Deep Reinforcement Learning (RL) algorithms can automatically learn context-dependent optimal parameter sets and dynamically adapt for a Weights-varying MPC (WMPC). However, learning cost function weights from scratch in a continuous action space may lead to unsafe operating states. To solve this, we propose a novel approach limiting the RL action space within a safe learning space that we represent by a catalog of pre-optimized feasible BO Pareto-optimal weight sets. We conceive an RL agent not to learn in a continuous space but to select the most optimal discrete actions, each corresponding to a single set of Pareto optimal weights, by proactively anticipating upcoming control tasks in a context-dependent manner. This approach introduces a two-step optimization: (1) safety-critical with BO and (2) performance-driven with RL. Hence, even an untrained RL agent guarantees a safe and optimal performance. Simulation results demonstrate that an untrained RL-WMPC shows Pareto-optimal closed-loop behavior and training the RL-WMPC helps exhibit a performance beyond the Pareto-front. The code used in this research is publicly accessible as open-source software: https://***/bzarr/TUM-CONTROL
This paper presents a new method for the autonomous landing of an unmanned aerial vehicle (UAV) on a moving ocean platform in simulation. The method uses GNSS data and a lightweight boat detection algorithm to guide t...
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