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Changes in gait asymmetry may be caused by adaptation of spinal reflexes

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Journal of Neurophysiology 2025年第4期133卷 1208-1215页

作者： Refy, Omar Mo, Owen Weber, Douglas J. Geyer, Hartmut Physics Department Carnegie Mellon University Pittsburgh PA United States Mechanical Engineering Department Carnegie Mellon University Pittsburgh PA United States Robotics Institute Carnegie Mellon University Pittsburgh PA United States Neuroscience Institute Carnegie Mellon University Pittsburgh PA United States NeuroMechatronics Lab Carnegie Mellon University Pittsburgh PA United States Legged Systems Lab Carnegie Mellon University Pittsburgh PA United States School of Computer Science Carnegie Mellon University Pittsburgh PA United States

In a recent human study, we found that adaptive changes in step length asymmetry (SLA) are correlated with similar changes in the H-reflex gains of the leg muscles during split-belt treadmill locomotion. Although this observation indicated a closer link between gait asymmetry and spinal reflex adaptation, it did not reveal their causal relationship. To better understand this relationship, here we use a neuromuscular model of human walking whose control relies primarily on spinal reflexes. Subjecting the model to split-belt treadmill locomotion with different combinations of belt speed and reflex gain patterns, we find that belt speed changes increase the variability in SLA but do not result in consistent SLA patterns as observed in human experiments, whereas reflex gain changes do. Furthermore, we find that the model produces SLA patterns similar to healthy adults when its reflex gains are adapted in a way similar to the H-reflex changes we observed in our previous study. The model also predicts SLA patterns similar to the ones observed for cerebellar degeneration patients when the reflexes do not adapt beyond a sudden dip at the time the ipsilateral belt speed is lowered. Our results suggest that SLA does not arise from imposing belt speed changes but requires the change of the reflex gains and that the dynamic adjustment of these gains may be an essential part of human gait control when encountering unexpected environmental changes such as uneven speed changes in split-belt treadmill locomotion. NEW & NOTEWORTHY This work uses computational modeling to investigate the role of spinal reflex tuning during locomotor adaptation. We show, in simulation, that tuning spinal reflex gains leads to gait asymmetry adaptation, not vice versa, and that patterns of gait adaptation on a split-belt treadmill are mostly driven by tuning of spinal reflexes, and not by biomechanical disturbances triggered by belt changes. The model further hints at the cerebellum as the source of sp

关键词： computational modeling gait adaptation neuromuscular model spinal reflex split-belt

Multi-Object Active Search and Tracking by Multiple Agents in Untrusted, Dynamically Changing Environments

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arXiv 2025年

作者： Jeong, Mingi Molinaro, Cristian Deb, Tonmoay Zhang, Youzhi Pugliese, Andrea Santos, Eugene Subrahmanian, V.S. Li, Alberto Quattrini Department of Computer Science Dartmouth College HanoverNH03755 United States DIMES University of Calabria Arcavacata87036 Italy Department of Computer Science Northwestern University EvanstonIL60208 United States Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Science Hong Kong Thayer School of Engineering Dartmouth College HanoverNH03755 United States

This paper addresses the problem of both actively searching and tracking multiple unknown dynamic objects in a known environment with multiple cooperative autonomous agents with partial observability. The tracking of a target ends when the uncertainty is below a specified threshold. Current methods typically assume homogeneous agents without access to external information and utilize short-horizon target predictive models. Such assumptions limit real-world applications. We propose a fully integrated pipeline where the main novel contributions are: (1) a time-varying weighted belief representation capable of handling knowledge that changes over time, which includes external reports of varying levels of trustworthiness in addition to the agents involved;(2) the integration of a Long Short Term Memory-based trajectory prediction within the optimization framework for long-horizon decision-making, which accounts for trajectory prediction in time-configuration space, thus increasing responsiveness;and (3) a comprehensive system that accounts for multiple agents and enables information-driven optimization during both the search and track tasks. When communication is available, our proposed strategy consolidates exploration results collected asynchronously by agents and external sources into a headquarters, who can allocate each agent to maximize the overall team’s utility, effectively using all available information. We tested our approach extensively in Monte Carlo simulations against baselines, representative of classes of approaches from the literature, and in robustness and ablation studies. In addition, we performed experiments in a 3D physics based engine robot simulator to test the applicability in the real world, as well as with real-world trajectories obtained from an oceanography computational fluid dynamics simulator. Results show the effectiveness of our proposed method, which achieves mission completion times that are 1.3 to 3.2 times faster in finding all tar

关键词： Multipurpose robots

A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

Predicting Salinity Resistance of Rice at the Seedling Stage: An Evaluation of Transfer Learning Methods 1st

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Predicting Salinity Resistance of Rice at the Seedling Stage...

1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Shiragudikar, Sharada K. Bharamagoudar, Geeta K., Manohara K. Y., Malathi S. G.Totad, Shashikumar School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India CSE Department KLEIT Karnataka Hubballi India Central Coastal Agricultural Research Institute Old Goa India Department of Automation and Robotics KLE Technological University Hubballi India

ISBN: (纸本)9783031756047

Rice has become one of the largest and most widely cultivated food crops in the world. Nevertheless, there is a significant probability that high amounts of salt, especially at the stage of seedling development, would have a detrimental effect on rice output. As a result, it is critical to quickly discover and develop salinity-tolerant rice crop types, particularly at the stage of seedling development, to prevent a decrease in rice output. Expertise from humans is required for the categorization of visual signals and the conventional way of a standard assessment system for identifying rice crop salinity stress. It's time demanding and prone to mistakes, both of which may lead to inaccurate classification. Recent developments in deep learning and transfer training have opened the road for the automated categorization of a wide range of agricultural jobs, from the detection of plant diseases and pests to the identification of weeds and invasive species. The lack of plant-domain-specific pre-trained models and the limited number of available datasets remain significant obstacles to agricultural automation. We use the AgriNet dataset, a database of 850 image graphs of farms and agricultural settings. The work shows the need for a deep learning developed model over the conventional way of measuring rice crop sensitivity to salt stress throughout the seedling stage to detect and categorize stress caused by salinity in the seedling stage of rice using field images. Thus, we use one of the most advanced deep learning approaches, VGG 16, VGG 19, Resnet 50, Resnet 100, exception, Resnet 50V2, Inception V3, MobileNetV2, DenseNet121, and ResNet101V2 which was built in Python's Jupyter Notebook, to construct the classification model. To further demonstrate the need for a computer-based classification system for salinity estimation that could be used as a tool for automating the classification process for rice development to aid scientists and farmers in the rice crop management

关键词： Deep learning

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

Model Predictive Path Integral Control of I2RIS Robot Using RBF Identifier and Extended Kalman Filter

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arXiv 2025年

作者： Esfandiari, Mojtaba Du, Pengyuan Wei, Haochen Gehlbach, Peter Munawar, Adnan Kazanzides, Peter Iordachita, Iulian Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States

Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (I2RIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models’ challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models’ performance during snake robots’ interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the I2RIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm. © 2025, CC BY.

关键词： Extended Kalman filters

Bio-Skin: A Cost-Effective Thermostatic Tactile Sensor with Multi-Modal Force and Temperature Detection

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arXiv 2025年

作者： Guo, Haoran Wang, Haoyang Li, Zhengxiong Tao, Lingfeng Oklahoma State University Mechatronics and Intelligent Robotics Lab 563 Engineering North StillwaterOK74075 United States University of Colorado Denver Department of Computer Science and Engineering 1380 Lawrence St. Center LW-834 DenverCO80217 United States

Tactile sensors can significantly enhance the perception of humanoid robotics systems by providing contact information that facilitates human-like interactions. However, existing commercial tactile sensors focus on improving the resolution and sensitivity of single-modal detection with high-cost components and densely integrated design, incurring complex manufacturing processes and unaffordable prices. In this work, we present Bio-Skin, a cost-effective multi-modal tactile sensor that utilizes single-axis Hall-effect sensors for planar normal force measurement and bar-shape piezo resistors for 2D shear force measurement. A thermistor coupling with a heating wire is integrated into a silicone body to achieve temperature sensation and thermostatic function analogous to human skin. We also present a cross-reference framework to validate the two modalities of the force sensing signal, improving the sensing fidelity in a complex electromagnetic environment. Bio-Skin has a multi-layer design, and each layer is manufactured sequentially and subsequently integrated, thereby offering a fast production pathway. After calibration, Bio-Skin demonstrates performance metrics—including signal-to-range ratio, sampling rate, and measurement range—comparable to current commercial products, with one-tenth of the cost. The sensor’s real-world performance is evaluated using an Allegro hand in object grasping tasks, while its temperature regulation functionality was assessed in a material detection task. Copyright © 2025, The Authors. All rights reserved.

关键词： Tactile sensors

Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

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Electric Vehicle Charging Demand Prediction using Multiresol...

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

Solar-Powered Smart Wheelchair with Voice Controller for Physically Challenged People

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Solar-Powered Smart Wheelchair with Voice Controller for Phy...

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha L. Sreevidya R. Deepalakshmi K Premalatha Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

It is challenging for those with strong mental faculties but physical disabilities to get around in a conventional hand-powered wheelchair. This project will enable any existing wheelchair to have an inexpensive assembly that enables a smart motion automation system that can be controlled by speech recognition on a smartphone. Continuous operation is guaranteed by the dual-charging system, which also offers longer mobility without the need for frequent recharging and independence from external power outlets. This enables users to adjust to varying conditions by depending on solar power during the day and switching to battery charging when solar energy is scarce. The main concept is a smartphone with built-in Bluetooth connectivity and the Android operating *** the distribution of the application we have developed, the goal of our project may be extended to other mobile devices that run Android software. The second main component of our system design is the microprocessor, which drives the wheelchair's dc motor in different directions. The wheelchair may now travel in a variety of directions thanks to this. The suggested model operates autonomously and on its own.

关键词： Automation Gears Navigation Wheelchairs Operating systems Wheels Solar energy DC motors Mobile handsets Telecommunication computing