Evidence map›Paper›PMID 41833946›Full record

ReviewNature communications2026

Embodying physical computing into soft robots.

Jun Wang, Ziyang Zhou, Ardalan Kahak, Suyi Li

Abstract readReview
In one paragraph

Review in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Stochastic Entanglement of Deterministic Origami Tentacles For Robust Robotic Grasping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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

4 authors.

Jun Wang *Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA. junw@vt.edu.ORCID http://orcid.org/0000-0003-3878-6195
Ziyang Zhou *Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA.ORCID http://orcid.org/0009-0003-9050-5431
Ardalan Kahak *Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA.
Suyi LiDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA. suyili@vt.edu.ORCID http://orcid.org/0000-0002-0355-1655

Funding

National Science Foundation (NSF) CMMI-2312422National Science Foundation (NSF) CMMI-2328522NSF | ENG/OAD | Division of Emerging Frontiers in Research and Innovation (EFRI) EFRI-2422340Virginia Polytechnic Institute and State University (Virginia Tech) Startup Fund
6 · The paper itself

Abstract

Softening and onboarding computers and controllers is one of the final frontiers in soft robotics towards their robustness and intelligence for everyday use. In this regard, embodying soft and physical computing presents exciting potential. Physical computing seeks to encode inputs into a mechanical computing kernel and leverage the internal interactions among this kernel's constituent elements to compute the output. Moreover, such input-to-output evolution can be re-programmable. This perspective paper proposes a framework for embodying physical computing into soft robots and discusses three unique strategies in the literature: analog oscillators, physical reservoir computing, and physical algorithmic computing. These embodied computers enable the soft robot to perform complex behaviors that would otherwise require CMOS-based electronics - including coordinated locomotion with obstacle avoidance, payload weight and orientation classification, and programmable operation based on logical rules. This paper will detail the working principles of these embodied physical computing methods, survey the current state-of-the-art, and present a perspective for future development.

Identifiers

PMID41833946
PMCPMC12992611

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.