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Passive adaptive grippers: a mini-review
Ming Chun Chan1, Rob B N Scharff1,2
1Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Frontiers in Robotics and AI
|May 25, 2026
Summary
Passive adaptive grippers use a host system's degrees of freedom (DOFs) for object manipulation. This review classifies designs, highlighting challenges in achieving reliable prehension, retention, and release for advanced robotic applications.
Area of Science:
- Robotics
- Mechanical Engineering
- Materials Science
Background:
- Passive adaptive grippers utilize a host system's degrees of freedom (DOFs) for manipulation.
- Embodied intelligence and environmental interaction are key to gripper function: prehension, retention, and release.
- State-of-the-art passive gripper designs are classified across three gripping phases.
Purpose of the Study:
- To establish a framework for classifying passive adaptive gripper designs.
- To accelerate research and guide application-specific design of passive grippers.
- To identify challenges and future research directions in passive gripper technology.
Main Methods:
- Classification of existing passive gripper designs.
- Analysis of gripper performance across prehension, retention, and release phases.
- Review of state-of-the-art literature on passive adaptive grippers.
Main Results:
- A classification framework for passive grippers is established, categorizing designs by prehension, retention, and release mechanisms.
- Fully passive grippers combining reliable prehension, internal retention, and contactless release are currently scarce.
- A trade-off exists between gripper controllability and host system flexibility, necessitating task-specific design.
Conclusions:
- Future research should focus on developing passive preloading mechanisms to minimize activation force.
- Optimal passive gripper design requires tailoring to specific tasks and operational constraints.
- Addressing the scarcity of fully passive grippers with combined functionalities is a key challenge.

