A MATLAB inverse-kinematics pipeline automated generation of 38 of 52 LIS signs on Pepper and cut authoring time from 45–60 to under 10 minutes per sign¶
Claim · All claims
Evidence · 1 study (2 entries) · designr3·q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? The semi-automated pipeline successfully generated 38 of the 52 manually implemented signs (73%); ten failed due to asymmetric or body-involving movements and four due to kinematic infeasibility or solver instability. → Giulia Boaa 2025
q2 i? Automated sign creation took under 10 minutes per sign versus approximately 45–60 minutes for manual creation. → Giulia Boaa 2025 (2)
Evidence¶
Giulia Boaa 2025¶
Giulia Boaa, Marco Boaa, Cristina Gena, Alessandro Mazzei, Massimo Donini, and Alberto Lillo. (2025). Using the Pepper Robot to Support Sign Language Communication. https://github.com/CodeCruncher63/ISCR2025 doi:10.1007/978-981-95-2382-5_8
q2 · i? · design · r3
Technical implementation result from the methodology section: a MATLAB inverse-kinematics solver plus Python exporter reproduced "38 out of the 52" signs; failures stemmed from asymmetric movements and solver instability.
"This approach successfully automated the genera0on of 38 out of the 52 manually implemented signs (73%). Ten signs could not be reproduced due to asymmetric or body-involving movements, and four others failed due to kinema0c infeasibility or solver instability."
Giulia Boaa 2025 (2)¶
Giulia Boaa, Marco Boaa, Cristina Gena, Alessandro Mazzei, Massimo Donini, and Alberto Lillo. (2025). Using the Pepper Robot to Support Sign Language Communication. https://github.com/CodeCruncher63/ISCR2025
q2 · i? · design · r3
Authoring-time comparison reported in the methodology section: manual sign creation took "approximately 45–60 minutes" versus "under 10 minutes per sign" with the automated pipeline.
"On average, the manual crea0on of a sign required approximately 45–60 minutes, while the automated method reduced this to under 10 minutes per sign, including coordinate setup and execu0on."
Discussion¶
Related Claims¶
- Recognition of individual Pepper-produced LIS signs varies widely, with several signs significantly above chance and others not significant — related
- LIS users recognize Pepper's isolated signs far better than its full signed sentences in an exploratory evaluation — related
- Thematic coding of sentence misinterpretations yields three categories: lexical confusion, structure misinterpretation, and partial recognition with wrong sequencing — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ik-pipeline-automates-pepper-lis-sign-generation |
title |
A MATLAB inverse-kinematics pipeline automated generation of 38 of 52 LIS signs on Pepper and cut authoring time from 45–60 to under 10 minutes per sign |
description |
A MATLAB inverse-kinematics pipeline automated generation of 38 of 52 LIS signs on Pepper and cut authoring time from 45–60 to under 10 minutes per sign |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2025 |
recheck_by |
2028 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — giulia-boaa-2025, giulia-boaa-2025-2 |
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