Vision-Based Robotic Grasping and Guided Control

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About this Research Topic

Submission deadlines

  1. Manuscript Summary Submission Deadline 1 March 2025 | Manuscript Submission Deadline 1 July 2025

  2. This Research Topic is still accepting articles.

    1. Check author guidelines

Background

Vision-based robotic grasping and guided control represent a pivotal area in robotics, combining advancements in computer vision, artificial intelligence, and control systems to enable robots to perceive, interpret, and interact with their environment. These technologies are instrumental in enabling robots to execute complex tasks such as precise object manipulation, flexible assembly, and adaptive task execution in dynamic and unstructured environments. Recent breakthroughs in deep learning, 3D vision, and multimodal perception have significantly enhanced the ability of robots to identify objects, estimate their poses, and plan effective grasping and manipulation strategies. Despite these advances, challenges remain in achieving reliable and efficient vision-based robotic control. Robots must operate robustly in scenarios involving occlusions, lighting variations, cluttered scenes, and deformable or complex-shaped objects. Moreover, integrating vision with real-time control mechanisms demands innovative algorithms and system architectures that balance accuracy, adaptability, and computational efficiency.

This research topic aims to provide a comprehensive platform for showcasing cutting-edge research and developments in vision-based robotic grasping and guided control. We invite contributions that address fundamental challenges, propose novel methodologies, and highlight practical implementations in this field. Specifically, this research topic seeks to: advance the state-of-the-art in visual perception techniques for robotic grasping, including object detection, segmentation, and pose estimation. Explore innovative grasp planning and control algorithms, emphasizing adaptability to complex and dynamic environments. Investigate the integration of multimodal sensory data (e.g., vision, force, and tactile) to enhance grasping and manipulation performance. Examine the role of human-robot interaction in guiding and improving robotic grasping systems, including intent recognition and shared control. Highlight applications and case studies that demonstrate the deployment of vision-based robotic grasping systems in real-world scenarios.

Some specific themes that potential contributors can address may include, but are not limited to:

- Vision-based object detection and segmentation for robotic grasping

- Real-time control systems for adaptive robotic manipulation

- Multimodal perception and sensor fusion for grasping

- Learning-based approaches for robust grasping in unstructured environments

- Human-robot collaboration in grasping and manipulation tasks

- Benchmarking datasets and evaluation metrics for robotic grasping

- Applications of vision-guided robotic grasping in industry and healthcare

- Sim-to-real transfer for vision-based grasping systems

- Lightweight algorithms and architectures for real-time deployment

- Grasping deformable, irregularly shaped, or transparent objects

- Vision-based robotic control in collaborative and multi-robot settings

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Data Report
  • Editorial
  • General Commentary
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion
  • Original Research

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Data Report
  • Editorial
  • General Commentary
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion
  • Original Research
  • Perspective
  • Policy and Practice Reviews
  • Review
  • Systematic Review
  • Technology and Code

Keywords: Robotic Grasping, Vision-Based Control, Perception and Manipulation, Multimodal Sensor Fusion, Human-Robot Collaboration, Adaptive Planning, Learning-Based Grasping, Real-Time Vision Systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Manuscripts can be submitted to this Research Topic via the main journal or any other participating journal.

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