Call For Papers

ICRTIS 2026 will provide a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Robotics.

The topics of interest for submission include, but are not limited to:

📚︎Topics 1: Intelligent Perception and Machine Vision

3D Visual Perception and Stereo Imaging

LiDAR and Environment Modeling

Optical Sensing and Multi-modal Fusion

Visual Servoing and Precision Manipulation

Spectral Imaging and Material Recognition

Computational Imaging and Visual Enhancement

Visual SLAM and Autonomous Navigation

Optoelectronic Inspection and Quality Monitoring

Deep Learning-based Visual Understanding

Bio-inspired Vision and Event Cameras

📚︎Topics 2: Autonomous Control and Intelligent Decision-making

Vision/Laser-guided Motion Planning and Dynamic Obstacle Avoidance

Multi-robot Cooperative Perception and Distributed Swarm Control

Human-Robot Interaction and Collaborative Control

Reinforcement Learning and Robotic Decision Control

Embedded Visual Intelligence and Edge Perception Computing

UAV Autonomous Control and Visual Navigation

Compliance Control and Force-feedback Teleoperation

Vision-based Digital Twin and Robotic System Simulation

Optoelectronic Sensing and Data-driven Fault Diagnosis

Cloud Robotics Visual Fusion and Networked Cooperative Control


📚︎Topics 3: Intelligent System Integration and Advanced Robotics Applications

Industrial Robots and Intelligent Manufacturing

Medical Robots and Rehabilitation Assistance

Service Robots and Human-Robot Coexistence

Agricultural Robots and Automation

Bio-inspired Vision and Soft Robotics Perception-Drive Integration

Micro-nano Manipulation and Precision Assembly

Autonomous Driving and Intelligent Vehicles

Special-purpose Robots and Rescue Applications

Robotic Intelligent Perception Hardware Architecture and Sensor Fusion

Embodied Intelligence and Physical Interaction Learning

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