Evidence map›Paper›PMID 41884001›Full record

ArticleiScience2026

Stereotypical force patterns of the elephant trunk in planar reaching movements.

Camilla Agabiti, Enrico Donato, Elisa Setti, Paule Dagenais, Michel C Milinkovitch, Cecilia Laschi, Angelo Maria Sabatini, Barbara Mazzolai, Egidio Falotico

Abstract read
In one paragraph

Article in iScience, 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

9 authors.

Camilla AgabitiBRAin-Inspired Robotics (BRAIR) Lab, The BioRobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56025 Pontedera, Italy.
Enrico DonatoBRAin-Inspired Robotics (BRAIR) Lab, The BioRobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56025 Pontedera, Italy.
Elisa SettiBRAin-Inspired Robotics (BRAIR) Lab, The BioRobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56025 Pontedera, Italy.
Paule DagenaisPhysics of Cells and Cancer, Institut Curie, Université Paris Sciences et Lettres (PSL), 75005 Paris, France.
Michel C MilinkovitchLaboratory of Artificial and Natural Evolution (LANE), Department of Genetics and Evolution, University of Geneva, Swiss Institute of Bioinformatics (SIB), 1211 Geneva, Switzerland.
Cecilia LaschiDepartment of Mechanical Engineering and Advanced Robotics Centre, National University of Singapore, Singapore 119077, Singapore.
Angelo Maria SabatiniBRAin-Inspired Robotics (BRAIR) Lab, The BioRobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56025 Pontedera, Italy.
Barbara MazzolaiBioinspired Soft Robotics Laboratory, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.
Egidio FaloticoBRAin-Inspired Robotics (BRAIR) Lab, The BioRobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56025 Pontedera, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The elephant trunk is a highly dexterous muscular hydrostat whose continuous, distributed deformations pose significant challenges for mathematical modeling. We introduce linear "stereotypical" laws that map desired trunk configurations, parameterized by curvature and length, directly to the internal muscle-analogue forces required in our rod-based dynamic model. The trunk is represented as a simplified multi-segment structure of point masses linked through longitudinal and radial muscle analogues and connective tissue, all modeled using rods. Using these laws, the model predicts biological reaching trajectories with tip-position errors below 8% while maintaining hydrostatic volume across trials. The resulting force-shape mappings reveal consistent, repeatable internal force patterns underlying trunk postures, providing a compact representation of actuation strategies that generate specific planar shapes. By reducing high-dimensional continuum dynamics to simple linear relationships, this framework preliminarily enables the inference of muscle-force distributions from shape configurations, laying the groundwork for deeper exploration of the elephant trunk motion strategies and their translation into advanced robotic systems control.

Indexed as

applied sciencesengineeringrobotics

Identifiers

PMID41884001
PMCPMC13010104

What OpenQuestion holds

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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.