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Design and Implementation of a Robotic Testbench for Analyzing Pincer Grip Execution in Human Specimen Hands

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Design and Implementation of a Robotic Testbench for Analyzi...

IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolas Wilhelm Claudio Glowalla Sami Haddadin Julian Schote Hannes Höppner Patrick van der Smagt Maximilian Karl Rainer Burgkart Department of Orthopedics and Sports Orthopedics Klinikum Rechts der Isar School of Medicine Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Humanoid Robotics Laboratory Berliner Hochschule für Technik Berlin Germany Machine Learning Research Lab Volkswagen Group Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This study presents an innovative test rig engineered to explore the kinematic and viscoelastic characteristics of human specimen hands. The rig features eight force-controlled motors linked to muscle tendons, enabling precise stimulation of hand specimens. Hand movements are monitored through an optical tracking system, while a force-torque sensor quantifies the resultant fingertip loads. Employing this setup, we successfully demonstrated a pincer grip using a cadaver hand and measured both muscle forces and grip strength. Our results reveal a nonlinear relationship between tendon forces and grip strength, which can be modeled by an exponential fit. This investigation serves as a nexus between biomechanical and robotics-focused research, providing critical insights for the advancement of robotic hand actuation and therapeutic interventions.

关键词： Biomechanics Technological innovation Tracking Stimulated emission Biological system modeling Muscles Motors

Changing functional connectivity during solving cognitive tasks: fNIRS study

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Changing functional connectivity during solving cognitive ta...

Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Badarin, A.A. Antipov, V.M. Grubov, V.V. Kurkin, S.A. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia Department of Theoretical Cybernetics Saint Petersburg State University University Embankment 7/9 Saint Petersburg199034 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

In this paper, we present an analysis of the dynamics of functional connectivity of the cerebral cortical network using near-infrared spectroscopy during human solutions to simple cognitive tasks. A task-based on the Sternberg paradigm was chosen to provide a cognitive load. We identified statistically significant changes in the communication forces obtained on the basis of the analysis of oxyhemoglobin signals during the experiment. We found that in the course of the experiment, there is a restructuring of the functional network, which is accompanied by a decrease in the average connectivity strength between the cortical areas under study. We showed that there is a correlation between the subjective evaluation of fatigue degree and the characteristics of the identified functional neural network. © 2022 SPIE.

关键词： Near infrared spectroscopy

Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation

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arXiv 2021年

作者： Zhang, Yang Wang, Yao Han, Zhi Chen, Xi'ai Tang, Yandong The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Center for Intelligent Decision-Making and Machine Learning School of Mangement Xi'an Jiaotong University Xi'An710049 China

In recent years, there have been an increasing number of applications of tensor completion based on the tensor train (TT) format because of its efficiency and effectiveness in dealing with higher-order tensor data. However, existing tensor completion methods using TT decomposition have two obvious drawbacks. One is that they only consider mode weights according to the degree of mode balance, even though some elements are recovered better in an unbalanced mode. The other is that serious blocking artifacts appear when the missing element rate is relatively large. To remedy such two issues, in this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called as tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods. The code is available at https://***/yzcv/TWMac-TT-OKA. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

Chaotic Change of Extracellular Matrix Molecules Concentration in the Presence of Periodically Varying Neuronal Firing Rate 20th

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Chaotic Change of Extracellular Matrix Molecules Concentrati...

20th International Conference on Mathematical Modeling and Supercomputer Technologies, MMST 2020

作者： Rozhnova, Maiya A. Bandenkov, Daniil V. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathemaics and Mechanics Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center of Neurotechnologies and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9783030787585

Transmission and processing of information in the brain are highly complicated processes that are defined by a lot of non-trivial interconnections of structural elements of neural networks. To shed light on peculiarities of such communication, a lot of mathematical models were introduced and computer simulations were carried out. In this work, a model describing the impact of neural activity on changes in brain extracellular matrix (ECM) molecules concentration was considered. It was assumed that the rate of neural activity is periodically changed. For this case, various regular and chaotic modes in dynamics of ECM-molecules concentration were observed. The role of the amplitude and frequency of the periodically varying neuronal firing rate in transitions between various dynamical modes in the ECM-model were examined. Bifurcational mechanisms for chaotic oscillations appearance were demonstrated. © 2021, Springer Nature Switzerland AG.

关键词： Brain

Hybrid moving object detection system based on key frame extraction

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Hybrid moving object detection system based on key frame ext...

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yu Du Xueyao Wang Shi-Ting Wang Shuangshuang Xue Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University Beijing China School of Automation Science and Electronic Engineering (ASEE) Beihang University Beijing China Robotics Institute Carnegie Mellon University USA

In this paper we construct a hybrid moving object detection system. In this system, we first use the frame difference method to extract key frames in a given video sequence, then use the optical flow method and the HSV background subtraction method to extract the moving objects, respectively. We propose two hybrid methods: Improved Optical Flow method and Improved HSV Background Subtraction method. Experimental results have shown that our proposed system has strong robustness and be effective to detect moving objects in various conditions. We also introduce a human-computer interaction tool for selecting the focus area for users. This system will largely benefit for real-world video surveillance applications.

关键词： Computer vision Lighting Image motion analysis Object detection Optical imaging Accuracy Video sequences

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

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arXiv 2024年

作者： Char, Ian Chung, Youngseog Abbate, Joseph Kolemen, Egemen Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Princeton Plasma Physics Laboratory United States Department of Mechanical and Aerospace Engineering Princeton Plasma Physics Laboratory Princeton University United States Robotics Institute Carnegie Mellon University United States

Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it. As such, it is crucial that high quality models are developed to assist in overcoming these obstacles. In this work, we take an entirely data driven approach to learn such a model. In particular, we use historical data from the DIII-D tokamak to train a deep recurrent network that is able to predict the full time evolution of plasma discharges (or "shots"). Following this, we investigate how different training and inference procedures affect the quality and calibration of the shot predictions. © 2024, CC BY.

关键词： Forecasting

Exploration via Planning for Information about the Optimal Trajectory

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arXiv 2022年

作者： Mehta, Viraj Char, Ian Abbate, Joseph Conlin, Rory Boyer, Mark D. Ermon, Stefano Schneider, Jeff Neiswanger, Willie Robotics Institute United States Machine Learning Department Carnegie Mellon University United States Princeton Plasma Physics Laboratory United States Princeton University United States Computer Science Department Stanford University United States

Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Deep learning for Koopman-based Dynamic Movement Primitives

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arXiv 2023年

作者： Han, Tyler Henshaw, Carl Glen The Naval Center for Space Technology U.S. Naval Research Laboratory WashingtonDC20375 United States The Robotics and Machine Learning Section Naval Center for Space Technology U.S. Naval Research Laboratory WashingtonDC20375 United States

The challenge of teaching robots to perform dexterous manipulation, dynamic locomotion, or whole-body manipulation from a small number of demonstrations is an important research field that has attracted interest from across the robotics community. In this work, we propose a novel approach by joining the theories of Koopman Operators and Dynamic Movement Primitives to learning from Demonstration. Our approach, named Autoencoder Dynamic Mode Decomposition (aDMD), projects nonlinear dynamical systems into linear latent spaces such that a solution reproduces the desired complex motion. Use of an autoencoder in our approach enables generalizability and scalability, while the constraint to a linear system attains interpretability. Our results are comparable to the Extended Dynamic Mode Decomposition on the LASA Handwriting dataset but with training on only a small fractions of the letters. © 2023, CC BY.

关键词： Dynamic mode decomposition

learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots

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Learning Barrier-Certified Polynomial Dynamical Systems for ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Martin Schonger Hugo T. M. Kussaba Lingyun Chen Luis Figueredo Abdalla Swikir Aude Billard Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany School of Computer Science University of Nottingham UK Omar Al-Mukhtar University (OMU) Albaida Libya Learning Algorithms and Systems Laboratory EPFL Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots’ resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimization problem can be very complex or extremely conservative when the traditional linear parameter-varying formulation is used. Thus, different from previous approaches in the literature, we propose to use polynomial representations for DSs, which yields an optimization problem that can be tackled by sum-of-squares techniques. Finally, our approach can handle obstacle shapes that fall outside the scope of assumptions typically found in the literature concerning obstacle avoidance within the DS learning framework. Supplementary material can be found at the project webpage: https://***/abc-ds

关键词： Shape Scalability Perturbation methods Polynomials Collision avoidance Dynamical systems Task analysis