저널 정보

Robot Learning

저널 웹사이트를 보려면 로그인해 주세요
무료 가입으로 공식 사이트 조회, 마감 추적, 이메일 리마인더를 이용할 수 있습니다.
로그인 회원가입

출판사:
ELSP
ISSN:
2960-1436
조회:
4151
팔로우:
0

논문 모집

Robot Learning is an academic journal published by ELSP. (ISSN 2960-1436).

Robot Learning is a peer-reviewed open access journal focused on publishing original works in all areas of robot learning in both theoretical research and application achievement. Topics of interest include but are not limited to the following: Learning enhanced perception for robotics Learning enhanced robot planning Learning enhanced robot manipulation Learning enhanced robot control Learning method for human-robot coordination Learning method for multi-robot cooperation or confrontation Learning method for self-driving Learning based robotic warehousing Learning enhanced intelligent transportation Learning method for bionic robotics and medical robotics Learning method for UAV and USV Deep learning, imitation learning and reinforcement learning for robotic system Sim-to-Real transfer for robotic applications Dataset, benchmark and simulator for robotic learning Applications for robotic learning
최종 수정: Dou Sun ()

Special Issues

Special Issue on Intelligent Vision-Driven Robotics 투고 마감일: 2026-07-31 Special Issue Editors Dr. Peng Zhou, Great Bay University, China Prof. David Navarro-Alarcon, The Hong Kong Polytechnic University, China About This Special Issue Aims and Motivation Robotics is converging on an intelligent, vision-driven paradigm where precise geometry, robust control, and data-driven adaptation co-exist and reinforce one another. Prof. Liu’s oeuvre exemplifies this synthesis—from early uncalibrated visual servoing and grasp theory to modern soft/surgical autonomy and large-scale SLAM—providing a unifying backbone for next-generation robots that are safe, dexterous, and reliable in unstructured, visually complex environments. This invitation-only Special Issue honors Prof. Yunhui Liu’s enduring impact on intelligent, vision-driven robotics. His work—spanning uncalibrated visual servoing, grasping and fixturing theory, motion planning, soft and continuum manipulation, surgical robotics, large-scale SLAM and 3D vision, networked teleoperation, and learning-enabled autonomy—has consistently connected rigorous theory with deployable, closed-loop systems, closing the loop between sensing and action in real-world environments. The collection is authored by Prof. Liu’s friends, former students, and close collaborators, and reflects his profound influence on vision-centered robotic intelligence. Submission Policy Invitation-first. This Special Issue is primarily invitation-based; invited manuscripts will be reviewed on a rolling basis. Inquiries welcome. If you haven’t received an invitation but believe your work is a strong fit for vision-driven robotics, you’re welcome to email the Guest Editors with a brief summary. Depending on space and scope, we may be able to extend additional invitations. Authors are encouraged to add a brief note on the relationship between your submission and Prof. Liu's academic work—e.g., which ideas, methods, or perspectives served as motivation or inspiration—consistent with the article type. Scope and Themes We welcome contributions that tightly integrate visual perception (including 3D geometry) with control and learning to achieve robust, generalizable, and deployable autonomy. Visual servoing and perception-driven control: Uncalibrated/model-free schemes; eye-in-hand and fixed-camera control; observers without visual velocity; nonholonomic/mobile visual control; task-oriented and invariant visual features. Grasping, fixturing, and dexterous manipulation:Vision- and tactility-informed grasp analysis and fixture design; multimodal sensing; soft/variable-stiffness hands; compliant/origami-inspired grippers; in-hand and textile/cable manipulation with visual feedback; geometry-aware policies. Deformable, soft, and continuum robots: Visual/FBG-based shape sensing and reconstruction; deformation and shape servoing; constrained-environment modeling and control; hybrid model–data methods for perception–control fusion. Surgical robotics and medical applications: Vision-centric autonomy in MRI/OR-integrated systems; autonomous endoscopic view control; instrument/tissue perception and 3D reconstruction (stereo/NeRF/Gaussian splatting); integrated perception–planning–control for safe task autonomy. SLAM, 3D vision, and geometric learning: Point/line/vanishing-point geometry; LiDAR/visual–inertial/edge-based SLAM; transparent/reflective/medical surface reconstruction; calibration and metrology; neural and geometric scene representations for control. Networked and human-in-the-loop robotics: Internet-based teleoperation with haptics and QoS; cooperative teleoperation; AR/gaze-based interaction; shared autonomy with intent inference; distributed estimation and coordination for multi-robot systems. Learning for vision-driven autonomy: Self-/weakly supervised visual representations for video and 3D; RL and imitation for manipulation, surgery, and locomotion with visual feedback; sim-to-real transfer; transformer/graph models coupling perception with planning and control; grounding policies in geometric priors. Field and industrial robotics: Vision-centric construction and finishing; warehouse fleets and swarm logistics; autonomous forklifts/AGVs and tractor–trailer control; robust bin picking and assembly with multi-view/active perception; long-horizon, closed-loop deployments. Article Types Original research articles with strong theoretical and experimental validation (bench-top to clinical/field), emphasizing vision-in-the-loop autonomy. System and integration papers demonstrating deployable, vision-driven, closed-loop performance in real applications. Survey/tutorial papers synthesizing state of the art at the intersection of vision, learning, and control, with clear roadmaps for future research. Benchmark/dataset papers that enable reproducibility and accelerate vision-based robotics, including protocols, metrics, code, and models. Intended Audience Researchers and practitioners in robotics, computer vision, control, and AI/ML for robotics; surgical/medical robotics; industrial and field automation; and human–robot interaction and teleoperation. Dedication It is an unforgettable memory and a great pleasure for many of us to have collaborated with Prof. Yunhui Liu—and, for some, to have worked under his mentorship. In deepest respect for his strong and inquiring mind, his enthusiasm for scientific inquiry, and his passion for education, we dedicate this Special Issue to him.
최종 수정: Dou Sun ()

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
AI+ELSP3007-7443
BlockchainELSP2959-1260
Advanced Information and CommunicationELSP3106-1443
Mechatronics TechnologyELSP2959-376X
Advanced ManufacturingELSP2959-3263
AI & MaterialsELSP3006-7588
Electronics and Signal ProcessingELSP2959-913X
Biomedical InformaticsELSP3005-3862
Artificial Intelligence and Autonomous SystemsELSP2959-0744
Quantum ResearchELSP3078-2902

관련 학회

CCFICORE약칭정식 명칭투고 마감통보일개최일
L&TInternational Learning and Technology Conference2024-10-122024-11-022025-01-15
ICRSAInternational Conference on Robot Systems and Applications2026-09-202026-10-202026-10-30
ICCRTInternational Conference on Control and Robot Technology2026-06-102026-07-102026-08-18
RiTAInternational Conference on Robot Intelligence Technology and Applications2020-07-012020-08-012020-10-30
A*HRIInternational Conference on Human-Robot Interaction2026-09-182027-03-08
ACIRSAsia-Pacific Conference on Intelligent Robot Systems2026-06-202026-07-152026-08-21
RPICInternational Conference on Robot Perception and Intelligent Control2026-11-082027-01-22
SYROCOIFAC Symposium on Robot Control2018-05-062018-06-032018-08-27
CRVCanadian Conference on Computer and Robot Vision2018-02-092018-03-112018-05-09
ARKInternational Symposium on Advances in Robot Kinematics2018-01-142018-02-282018-07-01

댓글 0

아직 댓글이 없습니다.

댓글을 작성하려면 로그인해 주세요