RoboPaper Atlas

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summary 2 cols 23 rows

FieldValue
Citations as of2026-04-28
Total papers91303
T-RO3435
IJRR2656
Sci-Rob886
T-FR85
SoRo823
T-Mech6065
T-ASE5506
RAM1688
RA-L10205
RA-P19
RSS1472
ICRA30612
IROS26594
CoRL1257
Year range1984 ~ 2026
With DOI90046 (98.6%)
With abstract88694 (97.1%)
With citation count90032 (98.6%)
Total citations3281136
Mean citations36.4
Median citations14

by_year_pivot 16 cols 43 rows

yearCoRLICRAIJRRIROSRA-LRA-PRAMRSSSci-RobSoRoT-ASET-FRT-MechT-ROtotal
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by_year_detail 8 cols 295 rows · showing first 30

yearvenuepaperswith_doiwith_abstracttotal_citationsmean_citationsabstract_coverage_%
2026IJRR2323221496.595.7
2026RA-L8028027127020.988.8
2026RA-P15151370.586.7
2026RAM21211180.452.4
2026Sci-Rob292929180.6100.0
2026SoRo39393840.197.4
2026T-ASE418418373630.289.2
2026T-FR99760.777.8
2026T-Mech2022021681270.683.2
2026T-RO8585732903.485.9
2025CoRL263000—0.0
2025ICRA16041604157691175.798.3
2025IJRR989898476048.6100.0
2025IROS19851985198138691.999.8
2025RA-L15771577148768254.394.3
2025RA-P44420.5100.0
2025RAM8686472653.154.7
2025RSS163163996734.160.7
2025Sci-Rob13013013012089.3100.0
2025SoRo7676621301.781.6
2025T-ASE12251225119738413.197.7
2025T-FR4343431363.2100.0
2025T-Mech62462459114292.394.7
2025T-RO36436432930928.590.4
2024CoRL265000—0.0
2024ICRA1760176017592102311.999.9
2024IJRR949492229924.597.9
2024IROS15811581158090875.799.9
2024RA-L1490149014831649311.199.5
2024RAM8080756347.993.8
+ 265 more rows — download the xlsx to see all 295.

top_cited_100 6 cols 100 rows · showing first 30

venueyeartitleauthorscited_by_countdoi
IJRR2013Vision meets robotics: The KITTI datasetAndreas Geiger; Philip Lenz; Christoph Stiller; Raquel Urtasun1023510.1177/0278364913491297
T-RO2015ORB-SLAM: A Versatile and Accurate Monocular SLAM SystemRaul Mur-Artal; J. M. M. Montiel; Juan D. Tardós720610.1109/tro.2015.2463671
IROS2012MuJoCo: A physics engine for model-based controlEmanuel Todorov; Tom Erez; Yuval Tassa706310.1109/iros.2012.6386109
T-RO2017ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D CamerasRaul Mur-Artal; Juan D. Tardós625710.1109/tro.2017.2705103
IJRR2011Sampling-based algorithms for optimal motion planningSertac Karaman; Emilio Frazzoli537710.1177/0278364911406761
ICRA20113D is here: Point Cloud Library (PCL)Radu Bogdan Rusu; Steve Cousins528710.1109/icra.2011.5980567
ICRA1991Object modeling by registration of multiple range imagesYang Chen; Gérard G. Medioni468210.1109/robot.1991.132043
T-RO2018VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State EstimatorTong Qin; Peiliang Li; Shaojie Shen416710.1109/tro.2018.2853729
RAM2006Simultaneous localization and mapping: part IHugh F. Durrant-Whyte; Tim Bailey415010.1109/mra.2006.1638022
IROS2012A benchmark for the evaluation of RGB-D SLAM systemsJürgen Sturm; Nikolas Engelhard; Felix Endres; Wolfram Burgard; Daniel Cremers411810.1109/iros.2012.6385773
ICRA1999Randomized Kinodynamic PlanningSteven M. LaValle; James J. Kuffner Jr.410810.1109/robot.1999.770022
ICRA2009Fast Point Feature Histograms (FPFH) for 3D registrationRadu Bogdan Rusu; Nico Blodow; Michael Beetz406510.1109/robot.2009.5152473
T-RO2021ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial, and Multimap SLAMCarlos Campos; Richard Elvira; Juan J. Gómez Rodríguez; José M. M. Montiel; Juan D. Tardós401710.1109/tro.2021.3075644
ICRA2000RRT-Connect: An Efficient Approach to Single-Query Path PlanningJames J. Kuffner Jr.; Steven M. LaValle396210.1109/robot.2000.844730
RAM1997The dynamic window approach to collision avoidanceDieter Fox; Wolfram Burgard; Sebastian Thrun387910.1109/100.580977
IROS2004Design and use paradigms for Gazebo, an open-source multi-robot simulatorNathan P. Koenig; Andrew Howard373010.1109/iros.2004.1389727
IJRR1990Passive Dynamic WalkingTad McGeer368610.1177/027836499000900206
IJRR2013Reinforcement learning in robotics: A surveyJens Kober; J. Andrew Bagnell; Jan Peters368510.1177/0278364913495721
IROS2015VoxNet: A 3D Convolutional Neural Network for real-time object recognitionDaniel Maturana; Sebastian A. Scherer365110.1109/iros.2015.7353481
IROS2017Domain randomization for transferring deep neural networks from simulation to the real worldJosh Tobin; Rachel Fong; Alex Ray; Jonas Schneider; Wojciech Zaremba; Pieter Abbeel360810.1109/iros.2017.8202133
T-RO2016Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception AgeCesar Cadena; Luca Carlone; Henry Carrillo; Yasir Latif; Davide Scaramuzza; José Neira; Ian Reid; John J. Leonard331310.1109/tro.2016.2624754
IJRR2001Randomized Kinodynamic PlanningSteven M. LaValle; James J. Kuffner Jr.322110.1177/02783640122067453
RSS2014LOAM: Lidar Odometry and Mapping in Real-timeJi Zhang; Sanjiv Singh304310.15607/rss.2014.x.007
IJRR2025Diffusion policy: Visuomotor policy learning via action diffusionCheng Chi; Zhenjia Xu; Siyuan Feng; Eric Cousineau; Yilun Du; Benjamin Burchfiel; Russ Tedrake; Shuran Song287810.1177/02783649241273668
T-RO2004Coverage control for mobile sensing networksJorge Cortés; Sonia Martínez; Timur Karatas; Francesco Bullo286810.1109/tra.2004.824698
IROS1995Series elastic actuatorsGill A. Pratt; Matthew M. Williamson257410.1109/iros.1995.525827
T-RO2007Improved Techniques for Grid Mapping With Rao-Blackwellized Particle FiltersGiorgio Grisetti; Cyrill Stachniss; Wolfram Burgard253110.1109/tro.2006.889486
ICRA2011G2o: A general framework for graph optimizationRainer Kümmerle; Giorgio Grisetti; Hauke Strasdat; Kurt Konolige; Wolfram Burgard240810.1109/icra.2011.5979949
ICRA2003Biped walking pattern generation by using preview control of zero-moment pointShuuji Kajita; Fumio Kanehiro; Kenji Kaneko; Kiyoshi Fujiwara; Kensuke Harada; Kazuhito Yokoi; Hirohisa Hirukawa235810.1109/robot.2003.1241826
ICRA2011Minimum snap trajectory generation and control for quadrotorsDaniel Mellinger; Vijay Kumar231010.1109/icra.2011.5980409
+ 70 more rows — download the xlsx to see all 100.

papers 13 cols 91,303 rows · showing first 30

venueyeartitleauthorsabstractcited_by_countconceptsdoieepagesdblp_keyopenalex_idvenues_all
IJRR2026A novel electromagnetic variable stiffness actuator for robotic grinding: Design, modeling, optimization, and controlXu Tang; Jixiang Yang; Han DingRobotic grinding relies on precise force control to ensure material removal precision and surface quality, particularly in thin-walled workpieces with varying stiffness. This paper…6.0Actuator; Stiffness; Control engineering; Grinding; Control theory (sociology)10.1177/02783649251347661https://doi.org/10.1177/02783649251347661259-284journals/ijrr/TangYD26https://openalex.org/W4411582533IJRR
IJRR2026ATOM: Design and development of a novel two-actuator hybrid land-air robotHitesh Bhardwaj; Luke Soe Thura Win; Shane Kyi Hla Win; Xinyu Cai; Shaohui FoongThis paper introduces a novel robot designed to exhibit two distinct modes of mobility: rotational aerial flight and terrestrial locomotion. This versatile robot comprises a sturdy…2.0Actuator; Robot; Atom (system on chip); Development (topology); Control engineering10.1177/02783649251344968https://doi.org/10.1177/0278364925134496880-99journals/ijrr/BhardwajWWCF26https://openalex.org/W4411134505IJRR
IJRR2026Analytical derivatives of strain-based dynamic model for hybrid soft-rigid robotsAnup Teejo Mathew; Frédéric Boyer; Vincent Lebastard; Federico RendaAlgorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabiliti…5.0Robot; Soft robotics; Computer science; Strain (injury); Control theory (sociology)10.1177/02783649251346209https://doi.org/10.1177/02783649251346209128-158journals/ijrr/MathewBLR26https://openalex.org/W4411735231IJRR
IJRR2026Design of LIGHTDOG: A high payload-to-weight, hose-less hydraulic quadrupedal robotSeunghoon Shin; Seungwoo Hong; Min-Su Kim; Jun-Ho Oh; Hae-Won ParkThis paper presents LIGHTDOG, a torque-controlled, hydraulically-actuated quadrupedal robot designed for a high power-to-weight ratio and substantial payload capabilities. Hydrauli…1.0Payload (computing); Robot; Quadrupedalism; Engineering; Computer science10.1177/02783649251349225https://doi.org/10.1177/02783649251349225285-307journals/ijrr/ShinHKOP26https://openalex.org/W4411625853IJRR
IJRR2026Domains as objectives: Domain-uncertainty-aware policy optimization through explicit multi-domain convex coverage set learningWendyam Eric Lionel Ilboudo; Taisuke Kobayashi; Takamitsu MatsubaraUncertainty is inherent in real-world robotics problems, and any control framework must address it to succeed in practical applications. Reinforcement Learning is no different, and…0.0Domain (mathematical analysis); Set (abstract data type); Computer science; Mathematical optimization; Regular polygon10.1177/02783649251358844https://doi.org/10.1177/02783649251358844397-451journals/ijrr/IlboudoKM26https://openalex.org/W4413361989IJRR
IJRR2026Dynamic wind-up locomotion enabled by embodied intelligenceChang Liu; Mark M. PlecnikRepetitive subtasks of locomotion are offloaded from a conventional computer-actuator-sensor set-up to automatic mechanical processes. The subtasks considered are: (1) when out-of-…1.0Embodied cognition; Computer science; Engineering; Human–computer interaction; Control engineering10.1177/02783649251360814https://doi.org/10.1177/02783649251360814501-521journals/ijrr/LiuP26https://openalex.org/W4413841627IJRR
IJRR2026Exciting families of passive gaits in an elastic quadruped via natural motion manifold controlDavide Calzolari; Cosimo Della Santina; Alin Albu-SchäfferMotivated by the need for efficiency and robustness in repetitive robotic tasks such as locomotion, this study introduces the concept of Natural Motion Manifolds (NMMs) and present…3.0Motion (physics); Manifold (fluid mechanics); Control theory (sociology); Natural (archaeology); Control (management)10.1177/02783649251347305https://doi.org/10.1177/02783649251347305233-258journals/ijrr/CalzolariSA26https://openalex.org/W4411655366IJRR
IJRR2026GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac SimElia Bonetto; Chenghao Xu; Aamir AhmadPhotorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be ea…11.0Robotics; Artificial intelligence; Computer science; Human–computer interaction; Robot10.1177/02783649251346211https://doi.org/10.1177/02783649251346211204-232journals/ijrr/BonettoXA26https://openalex.org/W4411616910IJRR
IJRR2026Heterogeneous LiDAR dataset for benchmarking robust localization in diverse degenerate scenariosZhiqiang Chen; Yuhua Qi; Dapeng Feng; Xuebin Zhuang; Hongbo Chen; Xiangcheng Hu; Jin Wu; Kelin Peng; Peng LuThe ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometri…36.0Benchmarking; Lidar; Computer science; Artificial intelligence; Machine learning10.1177/02783649251344967https://doi.org/10.1177/027836492513449676-22journals/ijrr/ChenQFZCHWPL26https://openalex.org/W4411136809IJRR
IJRR2026Hierarchical task decomposition for execution monitoring and error recovery: Understanding the rationale behind task demonstrationsChristoph Willibald; Dongheui LeeMulti-step manipulation tasks where robots interact with their environment and must apply process forces based on the perceived situation remain challenging to learn and prone to e…5.0Computer science; Anomaly detection; Artificial intelligence; Task (project management); Cluster analysis10.1177/02783649251352112https://doi.org/10.1177/02783649251352112369-396journals/ijrr/WillibaldL26https://openalex.org/W7110322758IJRR
IJRR2026IMA-catcher: An IMpact-aware nonprehensile catching framework based on combined optimization and learningFrancesco Tassi; Jianzhuang Zhao; Gustavo J. G. Lahr; Luna Gava; Marco Monforte; Arren Glover; Chiara Bartolozzi; Arash AjoudaniRobotic catching of flying objects typically generates high-impact forces that might lead to task failure and potential hardware damages. This is accentuated when the object mass t…3.0Robot; Computer science; Quadratic programming; Task (project management); Control theory (sociology)10.1177/02783649251345851https://doi.org/10.1177/02783649251345851100-127journals/ijrr/TassiZLGMGBA26https://openalex.org/W4411467075IJRR
IJRR2026Inverse dynamics trajectory optimization for contact-implicit model predictive controlVince Kurtz; Alejandro Castro; Aykut Özgün Önol; Hai LinRobots must make and break contact with the environment to perform useful tasks, but planning and control through contact remains a formidable challenge. In this work, we achieve r…35.0Inverse dynamics; Trajectory; Dynamics (music); Model predictive control; Control theory (sociology)10.1177/02783649251344635https://doi.org/10.1177/0278364925134463523-40journals/ijrr/KurtzCOL26https://openalex.org/W4410879364IJRR
IJRR2026Learning to control a soft robotic manipulator under uncertainty and unforeseen changes in robot-environment interactionZhiqiang Tang; Peiyi Wang; Wenci Xin; Cecilia LaschiSafe and efficient robot–environment interaction is a critical yet challenging problem, particularly in the presence of uncertainty and unforeseen changes. Soft robotics, known for…3.0Robot manipulator; Robot; Control (management); Artificial intelligence; Control engineering10.1177/02783649251360254https://doi.org/10.1177/02783649251360254452-476journals/ijrr/TangWXL26https://openalex.org/W4412758114IJRR
IJRR2026Locomotion and self-reconfiguration autonomy for spherical freeform modular robotsYuxiao Tu; Guanqi Liang; Di Wu; Xinzhuo Li; Tin Lun LamModular robotic systems are multi-robot systems comprising numerous repeated modules and can transform into different configurations. Matching system configurations to a library en…3.0Control reconfiguration; Modular design; Self-reconfiguring modular robot; Robot; Autonomy10.1177/02783649251360360https://doi.org/10.1177/02783649251360360477-500journals/ijrr/TuLWLL26https://openalex.org/W4412886248IJRR
IJRR2026MOANA: Multi-radar dataset for maritime odometry and autonomous navigation applicationHyesu Jang; Wooseong Yang; Hanguen Kim; Dongje Lee; Yongjin Kim; Jinbum Park; Minsoo Jeon; Jaeseong Koh; Yejin Kang; Minwoo Jung; Sangwoo Jung; Ayoung KimMaritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic objects, and the requirement for…6.0Odometry; Artificial intelligence; Computer science; Radar; Computer vision10.1177/02783649251354897https://doi.org/10.1177/02783649251354897193-203journals/ijrr/JangYKLKPJKKJJK26https://openalex.org/W4412119085IJRR
IJRR2026NeuSE: Neural SE(3)-equivariant embedding for long-term object-based simultaneous localization and mappingJiahui Fu; Yilun Du; Kurran Singh; Joshua B. Tenenbaum; John J. LeonardWe present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localization and Mapping (SLAM) for consistent sp…0.0Equivariant map; Term (time); Embedding; Object (grammar); Artificial intelligence10.1177/02783649251355966https://doi.org/10.1177/02783649251355966159-189journals/ijrr/FuDSTL26https://openalex.org/W4413128117IJRR
IJRR2026On the learning-based control of continuum robots with provable robustness, efficiency, and generalizabilityPeng Yu; Ning TanRecent years have witnessed the remarkable advancements in Koopman-operator-based data-driven methods for continuum robot control. However, there is currently a paucity of both the…3.0Generalizability theory; Robustness (evolution); Robot; Artificial intelligence; Computer science10.1177/02783649251351046https://doi.org/10.1177/02783649251351046308-327journals/ijrr/YuT26https://openalex.org/W4411624905IJRR
IJRR2026On-body textile hysteresis estimation for personalized physical human-robot interactionConnor M. McCann; James Arnold; Carolin Lehmacher; Katia Bertoldi; Conor J. WalshNearly all soft wearable robots rely on textiles to distribute actuation forces to the human body; however, the mechanical hysteresis of these materials significantly complicates d…1.0Textile; Robot; Computer science; Artificial intelligence; Human–computer interaction10.1177/02783649251358840https://doi.org/10.1177/02783649251358840352-365journals/ijrr/McCannALBW26https://openalex.org/W4412587399IJRR
IJRR2026Robust contact-rich manipulation through implicit motor adaptationTeng Xue; Amirreza Razmjoo; Suhan Shetty; Sylvain CalinonContact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control.…2.0Computer science; Control engineering; Adaptation (eye); Control theory (sociology); Artificial intelligence10.1177/02783649251344638https://doi.org/10.1177/0278364925134463841-59journals/ijrr/XueRSC26https://openalex.org/W4411230144IJRR
IJRR2026Role specialization enables superior task performance by human dyads than individualsAsuka Takai; Qiushi Fu; Yuzuru Doibata; Giuseppe Lisi; Toshiki Tsuchiya; Keivan Mojtahedi; Toshinori Yoshioka; Mitsuo Kawato; Jun Morimoto; Marco SantelloIt is generally accepted that collaboration yields better performance than when the same task is performed by individuals operating alone. Is collaboration always superior to indiv…1.0Task (project management); Cognitive psychology; Computer science; Psychology; Human–computer interaction10.1177/02783649251363274https://doi.org/10.1177/02783649251363274522-537journals/ijrr/TakaiFDLTMYKMS26https://openalex.org/W4413615422IJRR
IJRR2026SHINE: Social homology identification for navigation in crowded environmentsDiego Martinez-Baselga; Oscar de Groot; Luzia Knödler; Luis Riazuelo; Javier Alonso-Mora; Luis MontanoNavigating mobile robots in social environments remains a challenging task due to the intricacies of human-robot interactions. Most of the motion planners designed for crowded and…6.0Identification (biology); Human–computer interaction; Computer science; Artificial intelligence; Communication10.1177/02783649251344639https://doi.org/10.1177/0278364925134463960-79journals/ijrr/MartinezBaselgaGKRAM26https://openalex.org/W4411340081IJRR
IJRR2026Sensor-based distributionally robust control for safe robot navigation in dynamic environmentsKehan Long; Yinzhuang Yi; Zhirui Dai; Sylvia L. Herbert; Jorge Cortés; Nikolay AtanasovWe introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilisti…16.0Robot; Computer science; Control (management); Control engineering; Engineering10.1177/02783649251352000https://doi.org/10.1177/02783649251352000328-351journals/ijrr/LongYDHCA26https://openalex.org/W4411936039IJRR
IJRR2026Why we must trust ourselvesAntonio Bicchi—0.0—10.1177/02783649251405464https://doi.org/10.1177/027836492514054643-5journals/ijrr/Bicchi26https://openalex.org/W7111381031IJRR
RA-L2026$\pi$-BA: Probabilistic Neural Bundle Adjustment With Iterative Cycle Optimization for Driving Scene ReconstructionYunxuan Mao; Dongkun Zhang; Lilu Liu; Yue Wang; Rong XiongUrban scene reconstruction under noisy camera poses remains a critical challenge for autonomous driving. While recent neural dense Bundle Adjustment (BA) methods have shown promisi…0.0Artificial intelligence; Computer science; Bundle adjustment; Robustness (evolution); Overfitting10.1109/lra.2026.3669066https://doi.org/10.1109/LRA.2026.36690666280-6287journals/ral/MaoZLWX26https://openalex.org/W7133217038RA-L
RA-L2026360DVO: Deep Visual Odometry for Monocular 360-Degree CameraXiaopeng Guo; Yinzhe Xu; Huajian Huang; Sai-Kit YeungMonocular omnidirectional visual odometry (OVO) systems leverage 360-degree cameras to overcome field-of-view limitations of perspective VO systems. However, existing methods, reli…0.0Artificial intelligence; Visual odometry; Monocular; Computer vision; Computer science10.1109/lra.2026.3655280https://doi.org/10.1109/LRA.2026.36552803079-3086journals/ral/GuoXHY26https://openalex.org/W7125251342RA-L
RA-L20263D Cal: An Open-Source Software Library for Depth Reconstruction on Vision-Based Tactile SensorsRohan Kota; Kaival Shah; J. Edward Colgate; Gregory ReardonTactile sensing plays a key role in enabling dexterous and reliable robotic manipulation, but realizing this capability requires substantial calibration to convert raw sensor readi…0.0Software; Computer science; Computer vision; Artificial intelligence; Computer graphics (images)10.1109/lra.2026.3673994https://doi.org/10.1109/LRA.2026.36739946248-6255journals/ral/KotaSCR26https://openalex.org/W7135241459RA-L
RA-L20263D Force Sensor-Based Multimodal Tactile Sensing for Underwater Robotic Adaptive GraspingYuchao Liu; Yibin Chen; Zijie Liu; Haihong Qin; Long Ren; Weipeng Li; Xuan Wu; Jiajie GuoUnderwater tactile sensing is critical for marine robots to reliably manipulate objects. However, harsh underwater environments bring in serious disturbances to sensor techniques.…0.0Underwater; Grippers; Artificial intelligence; Computer vision; Object (grammar)10.1109/lra.2026.3671565https://doi.org/10.1109/LRA.2026.36715655342-5349journals/ral/LiuCLQRLWG26https://openalex.org/W7134819968RA-L
RA-L20263D-Printed Volume Adjustable Socket for Above-Knee ProsthesesFederico Donadel; Ahmed Zohaib Zaidi; Arianna Menciassi; Linda PaternòTraditional prosthetic sockets are rigid, passive structures that cannot accommodate daily residual limb volume fluctuations and are produced through labor-intensive processes invo…0.0Volume (thermodynamics); Interface (matter); Computer science; Wearable computer; Displacement (psychology)10.1109/lra.2026.3662558https://doi.org/10.1109/LRA.2026.36625584673-4680journals/ral/DonadelZMP26https://openalex.org/W7128519789RA-L
RA-L20264D Radar-Inertial Odometry Based on Gaussian Modeling and Multi-Hypothesis Scan MatchingFernando Amodeo; Luis Merino; Fernando Caballero4D millimeter-wave (mmWave) radars are sensors that provide robustness against adverse weather conditions (rain, snow, fog, etc.), and as such they are increasingly used for odomet…3.0Odometry; Matching (statistics); Artificial intelligence; Gaussian; Radar10.1109/lra.2026.3675514https://doi.org/10.1109/LRA.2026.36755145773-5780journals/ral/AmodeoMC26https://openalex.org/W4405626314RA-L
RA-L2026A 3D Vision-Based Framework for Teleoperation and Dynamic Catching With a High-Speed Multi-Fingered HandXiaohang Shi; Qitong Guo; Ruoyu Jia; Chunxin Yang; Kenichi Murakami; Yuji YamakawaDriven by significant advancements in structure, sensors, and control algorithms, multi-fingered hand systems have received increasing attention from both academia and industry. Wh…0.0Teleoperation; Deformation (meteorology); Computer science; Grippers; Control engineering10.1109/lra.2026.3671563https://doi.org/10.1109/LRA.2026.36715635230-5237journals/ral/ShiGJYMY26https://openalex.org/W7134851229RA-L
+ 91,273 more rows — download the xlsx to see all 91,303.