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Recent Advances in Robotics: Perception, Intelligent Control and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 908

Special Issue Editors

Institute of Automation, Chinese Academy of Sciences, Beijing, China
Interests: mechatronics and robotics; measurement; deep learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing, China
Interests: high speed vision; 3D vision; intelligent sensor
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of deep learning techniques has significantly promoted advances in intelligent visual perception, which has shown great application potential in various fields. For example, vision perception is critical for autonomous robots in navigating complex environments without human intervention. Meanwhile, visual perception is increasingly used in industrial scenes such as high-speed measurement, quality inspection, parts grabbing, and assembly. This Special Issue aims to provide a platform for the exchange of research works, technical trends, and applications. The scope of these papers may encompass measurement instruments, surface defect inspection, applications in robotic visual perception, visual servo control, and applications of intelligent vision in robotic and industrial systems.

Dr. Hu Su
Prof. Dr. Qingyi Gu
Guest Editors

Manuscript Submission Information

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Keywords

  • automated manufacturing
  • visual perception industrials
  • applications in mechatronics and robotics
  • applications of artificial intelligence in industrial electronic systems

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Published Papers (1 paper)

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Research

36 pages, 6755 KiB  
Article
A Human–Robot Skill Transfer Strategy with Task-Constrained Optimization and Real-Time Whole-Body Adaptation
by Guanwen Ding, Xizhe Zang, Xuehe Zhang, Changle Li, Yanhe Zhu and Jie Zhao
Appl. Sci. 2025, 15(6), 3171; https://doi.org/10.3390/app15063171 - 14 Mar 2025
Viewed by 459
Abstract
Human–robot skill transfer enables robots to learn skills from humans and adapt to new task-constrained scenarios. During task execution, robots are expected to react in real-time to unforeseen dynamic obstacles. This paper proposes an integrated human–robot skill transfer strategy with offline task-constrained optimization [...] Read more.
Human–robot skill transfer enables robots to learn skills from humans and adapt to new task-constrained scenarios. During task execution, robots are expected to react in real-time to unforeseen dynamic obstacles. This paper proposes an integrated human–robot skill transfer strategy with offline task-constrained optimization and real-time whole-body adaptation. Specifically, we develop the via-point trajectory generalization method to learn from only one human demonstration. To incrementally incorporate multiple human skill variations, we encode initial distributions for each skill with Joint Probabilistic Movement Primitives (ProMPs) by generalizing the template trajectory with discrete via-points and deriving corresponding inverse kinematics (IK) solutions. Given initial Joint ProMPs, we develop an effective constrained optimization method to incorporate task constraints in Joint and Cartesian space analytically to a unified probabilistic framework. A double-loop gradient descent-ascent algorithm is performed with the optimized ProMPs directly utilized for task execution. During task execution, we propose an improved real-time adaptive control method for robot whole-body movement adaptation. We develop the Dynamical System Modulation (DSM) method to modulate the robot end-effector through iterations in real-time and improve the real-time null space velocity control method to ensure collision-free joint configurations for the robot non-end-effector. We validate the proposed strategy with a 7-DoF Xarm robot on a series of offline and real-time movement adaptation experiments. Full article
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