Evidence map›Paper›PMID 42183025›Full record

ReviewFrontiers in robotics and AI2026

Passive adaptive grippers: a mini-review.

Ming Chun Chan, Rob B N Scharff

Abstract readReview
In one paragraph

Review in Frontiers in robotics and AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ming Chun ChanDivision of Integrative Systems and Design, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Rob B N ScharffDivision of Integrative Systems and Design, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Passive adaptive grippers leverage existing degrees of freedom (DOFs) of an external host system such as a robotic arm to complete a manipulation task. These grippers commonly rely on embodied intelligence to achieve this goal, leveraging interaction between the gripper and the environment to trigger prehension, retention, and release of an object. This mini-review establishes a framework for classification of state-of-the-art passive gripper designs across three phases of the gripping procedure: passive prehension (contact-loaded or preloaded), passive retention (externally or internally-sustained), and passive release (contact-based or contactless). Hereby, this work aims to accelerate future research on passive adaptive grippers and provide guidance for application-specific gripper design. Fully passive grippers that simultaneously combine reliable prehension, internally-sustained retention, and contactless release remain scarce. A fundamental trade-off exists between the gripper's controllability and the host system's flexibility; optimal gripper design must therefore be tailored to the specific task and operational constraints. Another key challenge is to minimize the force required to be exerted on the object to activate passive prehension. A promising direction towards addressing this challenge is the development of passive preloading mechanisms.

Indexed as

adaptive graspingembodied intelligencepassive gripperssoft gripperssoft robotics

Identifiers

PMID42183025
PMCPMC13189958

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.