Conference Information

ICRMV 2027: International Conference on Robotics and Machine Vision

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ICRMV
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Submission Date:
2026-11-01 Due in 46 days
Notification Date:
2026-12-01
Conference Date:
2027-03-19
Location:
Haining, China
Years:
Viewed: 15632   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

50.5 / 100
Ranked #1,471 of 5,682 conferences · Top 26%

#63 of 469 in Robotics & Control #63 of 244 in Computer Vision & Pattern Recognition

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
67
Community attention (10%)
13
Public record completeness (15%)
55

Inputs used: Editions on record: 11 · Researchers who opened this page in the past 24 months: 5

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-15

Call For Papers

ICRMV 2027 (International Conference on Robotics and Machine Vision) is an academic conference held in Haining, China on 2027-03-19. The paper submission deadline is 2026-11-01. Acceptance notifications are sent on 2026-12-01.

The conference focuses on innovative and original research results in robotics, machine vision, and their integration through learning-based approaches. Both theoretical papers and experimental/simulation studies are welcome. Topics of interest include, but are not limited to: 1. Machine Learning in Robotics Supervised, unsupervised, and reinforcement learning for robotics Adaptive and autonomous robot control systems Robot learning from demonstration and imitation Learning-based motion planning and navigation Multi-robot learning and coordination Transfer learning and domain adaptation for robotic applications 2. Deep Learning in Robotics Deep reinforcement learning for robotic control Convolutional Neural Networks (CNNs) for robotic perception Recurrent Neural Networks (RNNs) for sequential robot tasks Deep learning for robot localization and mapping (SLAM) End-to-end learning for robotic manipulation and locomotion Self-supervised and semi-supervised learning for robotics 3. Machine Vision and Perception Image and video analysis for robotic applications 3D vision, structure from motion, and depth perception Visual tracking, object detection, and pose estimation Semantic and instance segmentation for scene understanding Vision-based manipulation, grasping, and tactile-visual integration Sensor fusion (vision, LiDAR, radar, IMU) and multi-modal perception 4. Robotics Fundamentals and Control Robot kinematics, dynamics, and motion planning Multi-robot systems, swarm robotics, and coordination Human-robot interaction and collaborative robotics Soft robotics, bio-inspired robotics, and reconfigurable robots Robot perception, state estimation, and sensor networks Real-time and embedded control architectures 5. Applications of Machine Learning and Vision in Robotics Healthcare, surgical, and rehabilitation robotics Industrial automation, manufacturing, and logistics Autonomous vehicles, drones, and intelligent transportation systems Service, social, and assistive robotics Agricultural, field, and forestry robotics Exploration, search and rescue, and planetary robotics 6. Theoretical Foundations and Real-World Deployments Advances in neural network architectures for robotic vision Statistical learning, scalability, and real-time efficiency Explainability, interpretability, and safety of robotic learning models Robustness, generalization, and domain adaptation in real-world environments Benchmarking, performance evaluation, and case studies of deployed systems Ethical considerations, legal frameworks, and human-centric design
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