Evidence map›Paper›PMID 41103202›Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2026

Physical Intelligence in Small-Scale Robots and Machines.

Huyue Chen, Metin Sitti

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Huyue ChenSchool of Medicine and College of Engineering, Koç University, Istanbul, 34450, Türkiye.
Metin SittiSchool of Medicine and College of Engineering, Koç University, Istanbul, 34450, Türkiye.ORCID 0000-0001-8249-3854

Funding

ERC Proof of Concept STENTBOT 101100727European Research Council 834531
6 · The paper itself

Abstract

Intelligent living organisms-from unicellular entities to plants-rely on body physical intelligence (PI) to autonomously adapt and thrive in dynamic and complex environments, bypassing neural processing. The paradigm of PI has become a pivotal framework for small-scale mobile robots and machines, where they have limited onboard powering, actuation, perception, computation, and control. However, the emerging PI capabilities remain rudimentary compared to biological counterparts in adaptability, multifunctionality, and evolvability. Here, the review systematically examines PI in small-scale mobile robots and machines, highlight the importance of PI in extreme environments, elucidate hierarchical PI manifestations, identify current challenges and future opportunities for further promoting the evolution of PI. Notably, Current research emphasizes that the human body, featuring confined spaces, active and uncertain fluid and organ movements, immunological reactions, and heterogeneous physicochemical conditions, can be an ultimate testing ground for the next-generation small-scale robotic systems with more advanced PI. Looking forward, the rapid evolution of PI benefits from the convergence of multiple disciplines, such as robotics, mechanics, materials, chemistry, biology, and medicine, toward creating autonomous intelligent machines for real-world applications.

Indexed as

Artificial IntelligenceIntelligenceRoboticsAnimalsHumansimplantable sensorsmechanical metamaterialsphysical intelligenceroboticssmart materials

Identifiers

PMID41103202
PMCPMC12933020

What Socratic 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 Socratic graph.