Leading the Pack: Next-Generation Batteries for Humanoid Robotics.

Matthew Bergschneider, Jiaqi Ke, Jin Luo, Laisuo Su, Thomas Campbell, Yue Zhou, Kyeongjae Cho

Abstract readReview

In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

7 authors.

Matthew BergschneiderDepartment of Materials Science and Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0000-0002-3242-2529

Jiaqi KeDepartment of Materials Science and Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0009-0003-1648-3545

Jin LuoDepartment of Mechanical Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0009-0007-7365-0791

Laisuo SuDepartment of Materials Science and Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0000-0002-9307-9357

Thomas CampbellLEAP Manufacturing, Evergreen, Colorado, USA.

Yue ZhouDepartment of Mechanical Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0000-0002-5060-5054

Kyeongjae ChoDepartment of Materials Science and Engineering, University of Texas at Dallas, Richardson, Texas, USA.ORCID https://orcid.org/0000-0003-2698-7774

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transitioning from demonstration to commercial deployment of humanoid robots offers the potential to accelerate multiple industries. As mechatronic and software demands increase, uninterrupted application requires a quadrupling of battery system volumetric and gravimetric capacity. First considered here are the unique challenges of the humanoid robot platform. The upper bounds of further optimized current generation battery materials, electrodes (electrode coatings, electrolytes, etc.), and battery design (cylindrical, pouch, or prismatic) can narrow the gap of practical capacities toward theoretical limits, but only so far. Application cases indicate energy demands of 10+ kWh, which will necessitate next-generation batteries with specific energy

Indexed as

batterieshumanoid roboticspower sources

Identifiers

PMID42524759

DOI10.1002/advs.76736

PMCPMC13418515

What OpenQuestion holds

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LicenceCC BY

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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.