Dexterous Manipulation and Adaptive Robotic Grasping: From Mechanisms to Intelligent Control

Manipulation is central to robotics applications ranging from industrial automation to service, logistics, healthcare, and human–robot collaboration. Conventional parallel-jaw grippers and rigid end-effectors are effective for structured tasks, but often lack adaptability when handling diverse objects, deformable items, or cluttered scenes. In response, the community has developed dexterous multi-finger hands, underactuated and compliant grippers, and soft robotic end-effectors that exploit morphology and compliance to simplify control while improving robustness.

At the same time, advances in tactile sensors, high-speed perception, and data-driven learning have enabled contact-aware manipulation, enabling robots to detect slip, estimate contact states, and adjust grasps online. However, challenges remain in modeling contact, achieving reliable generalization, and deploying systems that are both performant and safe in real environments. These trends motivate a broad, interdisciplinary forum for new ideas and validated systems.

Robotic manipulation has progressed rapidly, yet achieving reliable, human-like dexterity remains a key bottleneck for robots operating in open-world environments. Practical manipulation systems must not only grasp objects, but also regrasp, reposition, and manipulate them under uncertainty in shape, pose, friction, and contact dynamics. This calls for tight integration across hand/gripper mechanisms, compliance design, multimodal sensing (vision–tactile–proprioception), and robust control and planning.

The goal of this Research Topic is to gather recent advances that enable dexterous manipulation and adaptive grasping across robotic hands, grippers, and soft/compliant end-effectors. We aim to highlight methods spanning mechanical innovation, perception-driven grasp synthesis, contact-rich motion planning, and learning-based manipulation policies that generalize beyond curated benchmarks. Contributions that bridge simulation and real-world deployment, address safety and reliability, and demonstrate performance in unstructured scenarios are particularly encouraged. By connecting robotics, control, AI, and mechanical design communities, this topic seeks to accelerate progress toward versatile, deployable manipulation systems.

This Research Topic welcomes original research articles, reviews, and perspectives on theories, methods, and systems that advance robotic grasping and manipulation. We encourage contributions with rigorous evaluation in simulation and/or physical experiments, and value reproducibility, open benchmarks, and strong ablation/analysis when applicable. Relevant themes include (but are not limited to):

1.Design and modeling of dexterous robotic hands and grippers

2.Soft robotic manipulation and compliant grasping mechanisms

3.Adaptive and intelligent grasp planning algorithms

4.Learning-based manipulation and reinforcement learning for grasping

5.Tactile sensing and perception for robotic hands

6.Motion planning and control for dexterous manipulation

7.Human-inspired manipulation strategies

8.Applications of dexterous manipulation in industry, service robotics, and healthcare

Submissions may include theoretical studies, experimental validation, system design, and real-world robotic applications that contribute to advancing the field of robotic grasping and manipulation.

Article types and fees

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

• Brief Research Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

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
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Dexterous robotic hand; Robotic grippers; Adaptive grasping; Dexterous manipulation; Soft robotic manipulation; Grasp planning; Tactile sensing; Learning-based manipulation

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.