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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 711-722页

作者： Maldonado-Contreras, Jairo Johnson, Cole Zhou, Sixu Kim, Hanjun Knight, Ian Herrin, Kinsey R. Young, Aaron J. Georgia Institute of Technology Mechanical Engineering Department AtlantaGA30332 United States Georgia Institute of Technology Institute of Robotics and Intelligent Machines AtlantaGA30332 United States Georgia Institute of Technology School of Computer Science AtlantaGA30332 United States

This study introduces a novel continual learning algorithm that incrementally improves the performance of deep-learning-based walking speed estimators during level-ground walking with a powered knee-ankle prosthesis. While user-dependent (DEP) estimators generally outperform user-independent (IND) estimators, they require the pre-collection of DEP training data. In contrast, our real-time algorithm adapts IND estimators to self-labeled DEP data generated during walking, eliminating the need for pre-collected datasets. The algorithm also features a biomimetic scaling mechanism that adjusts prosthetic assistance based on speed estimates. We evaluated our algorithm on novel subjects (N=10) with unilateral above-knee amputations during treadmill and overground walking. For treadmill trials, when adapted with estimated and ground truth labels, estimators achieved mean absolute errors (MAEs) of 0.074 0.023 (mean, standard deviation) and 0.074 0.018 m/s, respectively, reflecting a significant 28% (p © 2018 IEEE.

关键词： Knee prostheses

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FoundationGrasp: Generalizable Task-Oriented Grasping With Foundation Models

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12418-12435页

作者： Tang, Chao Huang, Dehao Dong, Wenlong Xu, Ruinian Zhang, Hong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relationship between objects, tasks, and grasps neces-sitates rich semantic and geometric prior knowledge about these elements. Existing methods typically restrict the prior knowledge to a closed-set scope, limiting their generalization to novel objects and tasks out of the training set. To addresssuch a limitation, we propose FoundationGrasp, a foundationmodel-based TOG framework that leverages the open-ended knowledge from foundation models to learn generalizable TOG skills. Extensive experiments are conducted on the contributed Language and Vision Augmented TaskGrasp (LaViA-TaskGrasp)dataset, demonstrating the superiority of FoundationGrasp over existing methods when generalizing to novel object instances,object classes, and tasks out of the training set. Furthermore,the effectiveness of FoundationGrasp is validated in real-robot grasping and manipulation experiments on a 7-DoF robotic arm. © 2004-2012 IEEE.

关键词： Robotic arms

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Best Linear Controllers for Decentralized Linear Quadratic Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 210-237页

作者： AFSHARI Mohammad MAHAJAN Aditya Institute for Robotics and Inteligent Machines Georgia Institute of TechnologyAtlanta 30332-0530USA Department of Electrical and Computer Engineering McGill UniversityMontreal H3A 0G4Canada

In this paper,the authors revisit decentralized control of linear quadratic(LQ)*** of imposing an assumption that the process and observation noises are Gaussian,the authors assume that the controllers are restricted to be *** authors show that the multiple decentralized control models,the form of the best linear controllers is identical to the optimal controllers obtained under the Gaussian noise *** main contribution of the paper is the solution ***,optimal controllers for decentralized LQ systems are identified using dynamic programming,maximum principle,or spectral *** authors present an alternative approach which is based by combining elementary building blocks from linear systems,namely,completion of squares,state splitting,static reduction,orthogonal projection,(conditional)independence of state processes,and decentralized estimation.

关键词： Decentralized estimation decentralized stochastic control linear quadratic systems team theory

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Ankle Exoskeleton Control via Data-Driven Gait Estimation for Walking, Running, and Inclines

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IEEE robotics and Automation Letters 2025年第6期10卷 5855-5862页

作者： Shetty, Pavan R. Menezes, Jayston A. Song, Seungmoon Young, Aaron J. Shepherd, Max K. Northeastern University Department of Mechanical Engineering BostonMA02115 United States Northeastern University Department of Mechanical Engineering Institute for Experiential Robotics BostonMA02115 United States Georgia Institute of Technology Woodruff School of Mechanical Engineering Institute for Robotics and Intelligent Machines AtlantaGA30332-0405 United States Northeastern University Department of Mechanical Engineering Institute for Experiential Robotics Department of Physical Therapy Movement and Rehabilitation Science BostonMA02115 United States

Ankle exoskeletons have the potential to augment mobility, but control strategies have largely failed to seamlessly adapt to changes in the locomotion task. Here, we introduce a multi-headed network that predicts gait speed, ground incline, stance/swing transitions, and percent stance. These predictions are mapped to exoskeleton torque using typical biological torques as a guide. The model was trained on 9 subjects walking/jogging for 12 minutes across a range of speeds and inclines. The controller was validated on 4 subjects, and achieved stance phase prediction error of 3.4% across a range of speeds and inclines, both inside and outside the training set distribution. A secondary analysis showed similar accuracy could have been obtained with only 10% of the collected data, suggesting researchers may need fewer total strides of training data, provided the data is sufficiently diverse across users and tasks. Metabolic cost was improved during running compared to wearing the exoskeleton powered off, but was beneficial for only one subject during level walking and ramp ascent when compared to no exoskeleton. Overall, our controller smoothly adapted to time-varying inclines and walking/jogging speeds, and achieved high accuracy with a reduced training dataset, though larger torque magnitudes may be required to see metabolic benefit. © 2016 IEEE.

关键词： Exoskeleton (robotics)

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Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 666-677页

作者： Peterson, Reese R. Leestma, Jennifer K. Kang, Inseung Young, Aaron J. Georgia Institute of Technology Department of Mechanical Engineering AtlantaGA30332 United States Florida Institute for Human and Machine Cognition PensacolaFL32502 United States Georgia Institute of Technology Department of Mechanical Engineering The Institute for Robotics and Intelligent Machines AtlantaGA30332 United States Harvard University John A. Paulson School of Engineering and Applied Sciences AllstonMA02134 United States Massachusetts Institute of Technology The Department of Brain and Cognitive Sciences BostonMA02139 United States

Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton. We trained and evaluated user-independent models on early-stance (ES) and late-stance (LS) slips of various magnitudes collected through treadmill-based slips. All models, except LDA with LS slips, detected slips with >90% accuracy. Overall, the best model was XGBoost, with its fastest results achieving average detection times and median accuracies of 155.06 ms at 96.25% for ES slips and 228.88 ms at 93.75% for LS slips, while also achieving 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS). Our results indicate a promising direction for further research into designing a generalizable model for balance recovery during slip perturbations using robotic hip exoskeletons. © 2018 IEEE.

关键词： Exoskeleton (robotics)

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Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion

arXiv

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arXiv 2025年

作者： Knaup, Jacob D’sa, Jovin Chalaki, Behdad Mahjoub, Hossein Nourkhiz Moradi-Pari, Ehsan Tsiotras, Panagiotis Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332–0250 United States Honda Research Institute USA Inc. United States

Interactive decision-making is essential in applications such as autonomous driving, where the agent must infer the behavior of nearby human drivers while planning in real-time. Traditional predict-then-act frameworks are often insufficient or inefficient because accurate inference of human behavior requires a continuous interaction rather than isolated prediction. To address this, we propose an active learning framework in which we rigorously derive predicted belief distributions. Additionally, we introduce a novel model-based diffusion solver tailored for online receding horizon control problems, demonstrated through a complex, non-convex highway merging scenario. Our approach extends previous high-fidelity dual control simulations to hardware experiments, which may be viewed at https://***/Q_ JdZuopGL4, and verifies behavior inference in human-driven traffic scenarios, moving beyond idealized models. The results show improvements in adaptive planning under uncertainty, advancing the field of interactive decision-making for real-world applications. © 2025, CC BY.

关键词： Autonomous agents

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Human-in-the-loop Optimization of Hip Exoskeleton Assistance During Stair Climbing

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Park, Dongho An, Jimin Lee, Dawit Kang, Inseung Young, Aaron J. Woodruff School of Mechanical Engineering the Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0405 United States the Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332-0405 United States the Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States the Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States the Department of Bioengineering Stanford University StanfordCA94305 United States the Woodruff School of Mechanical Engineering the Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0405 United States

—This study applies human-in-the-loop optimization to identify optimal hip exoskeleton assistance patterns for stair climbing. Ten participants underwent optimization to individualize hip flexion and extension assistance, followed by a validation comparing optimized assistance (OPT) to biological hip moment-based assistance (BIO), no assistance (No-Assist), and no exoskeleton (No-Exo) conditions. OPT reduced metabolic cost by 4.5% compared to No-Exo, 11.44% compared to No-Assist, and 5.02% compared to BIO, demonstrating the effectiveness of the optimization approach. Statistical analysis revealed distinct characteristics in optimal assistance timing and magnitude that deviated systematically from biological hip moment patterns. Compared to BIO, OPT exhibited later peak flexion timing (76.4 ± 3.7% vs 65.0%), shorter flexion duration (29.2 ± 3.6% vs 40.0%), later peak extension timing (26.7 ± 3.8% vs 20.0% of gait cycle), and higher peak flexion magnitude (11.1 ± 1.5 Nm vs 10.0 Nm). While individual optimal assistance profiles varied across participants, comparison between individually optimized parameters and the best subject-independent parameters identified through post-hoc analysis showed consistency. On average, metabolic rate convergence was achieved after 18 iterations, while most exoskeleton control parameters did not reach our convergence criteria within 20 iterations. These findings demonstrate that human-in-the-loop optimization can effectively identify task-specific assistance patterns for stair climbing, while the consistency between individual and subject-independent parameters suggests the potential for developing generalized assistance strategies. The systematic differences between optimized and biological moment-based assistance underscore the fundamental distinctions between biological torque-based control and optimal control for exoskeleton assistance. © 2025 IEEE Computer Society. All rights reserved.

关键词： Exoskeleton (robotics)

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Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots

arXiv

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arXiv 2025年

作者： He, Juntao Chong, Baxi Nienhusser, Vincent R. Iaschi, Massimiliano Ha, Sehoon Goldman, Daniel I. Institute for Robotics and Intelligent Machines Georgia Institute of Technology United States School of Physics Georgia Institute of Technology United States School of Mechanical Engineering Georgia Institute of Technology United States College of Computing Georgia Institute of Technology United States

Many-legged elongated robots show promise for reliable mobility on rugged landscapes. However, most studies on these systems focus on motion planning in the 2D horizontal plane (e.g., translation and rotation) without addressing rapid vertical motion. Despite their success on mild rugged terrains, recent field tests reveal a critical need for 3D behaviors (e.g., climbing or traversing tall obstacles) in real-world application. The challenges of 3D motion planning partially lie in designing sensing and control for a complex high-degree-of-freedom system, typically with over 25 degrees of freedom. To address the first challenge, we propose a tactile antenna system that enables the robot to probe obstacles and gather information about the structure of the environment. Building on this sensory input, we develop a control framework that integrates data from the antenna and foot contact sensors to dynamically adjust the robot’s vertical body undulation for effective climbing. With the addition of simple, low-bandwidth tactile sensors, a robot with high static stability and redundancy exhibits predictable climbing performance in complex environments using a simple feedback controller. Laboratory and outdoor experiments demonstrate the robot’s ability to climb obstacles up to five times its height. Moreover, the robot exhibits robust climbing capabilities on obstacles covered with flowable, robot-sized random items and those characterized by rapidly changing curvatures. These findings demonstrate an alternative solution to perceive the environment and facilitate effective response for legged robots, paving ways towards future highly capable, low-profile many-legged robots. © 2025, CC BY.

关键词： Motion planning

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Stereophotoclinometry Revisited

arXiv

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arXiv 2025年

作者： Driver, Travis Vaughan, Andrew Cheng, Yang Ansar, Adnan Christian, John Tsiotras, Panagiotis Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States School of Aerospace Engineering Georgia Institute of Technology AtlantaGA United States Institute for Robotics and Intelligent Machines School of Aerospace Engineering Georgia Institute of Technology AtlantaGA United States

Image-based surface reconstruction and characterization is crucial for missions to small celestial bodies, as it informs mission planning, navigation, and scientific analysis. However, current state-of-the-practice methods, such as stereophotoclinometry (SPC), rely heavily on human-in-the-loop verification and high-fidelity a priori information. This paper proposes Photoclinometry-from-Motion (PhoMo), a novel framework that incorporates photoclinometry techniques into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery. In contrast to SPC, we forego the expensive maplet estimation step and instead use dense keypoint measurements and correspondences from an autonomous keypoint detection and matching method based on deep learning. Moreover, we develop a factor graph-based approach allowing for simultaneous optimization of the spacecraft’s pose, landmark positions, Sun-relative direction, and surface normals and albedos via fusion of Sun vector measurements and image keypoint measurements. The proposed framework is validated on real imagery taken by the Dawn mission to the asteroid 4 Vesta and the minor planet 1 Ceres and compared against an SPC reconstruction, where we demonstrate superior rendering performance compared to an SPC solution and precise alignment to a stereophotogrammetry (SPG) solution without relying on any a priori camera pose and topography information or humans-in-the-loop. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface reconstruction

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CBS-Budget (CBSB): A complete and bounded suboptimal search for multi-agent path finding

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Artificial Intelligence 2025年 346卷

作者： Lim, Jaein Tsiotras, Panagiotis The Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology AtlantaGA30332 United States The Institute for Robotics Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

Multi-Agent Path Finding (MAPF) is the problem of finding a collection of conflict-free paths for a team of multiple agents while minimizing some global cost, such as the sum of the travel time of all agents, or the travel time of the last agent. Conflict Based Search (CBS) is a leading complete and optimal MAPF algorithm that lazily explores the joint agent state space, using an admissible heuristic joint plan. Such an admissible heuristic joint plan is computed by combining individual shortest paths computed without considering inter-agent conflicts, and becoming gradually more informed as constraints are added to the individual agents' path-planning problems to avoid discovered conflicts. In this paper, we seek to speed up CBS by finding a more informed heuristic joint plan that is bounded. We first propose the budgeted Class-Ordered A* (bCOA*), a novel algorithm that finds the least-cost path with the minimal number of conflicts that is upper bounded in terms of path length. Then, we propose a novel bounded-cost variant of CBS, called CBS-Budget (CBSB) by using bCOA* search at the low-level search of the CBS and by using a modified focal search at the high-level search of the CBS. We prove that CBSB is complete and bounded-suboptimal. In our numerical experiments, CBSB finds a near-optimal solution for hundreds of agents within a fraction of a second. CBSB shows state-of-the-art performance, comparable to Explicit Estimation CBS (EECBS), an enhanced recent version of CBS. On the other hand, CBSB is much easier to implement than EECBS, since only one priority queue at the low-level search is needed, as in CBS, and only two priority queues at the high-level search are needed, as in Enhanced CBS (ECBS). © 2025 Elsevier B.V.

关键词： Travel time

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