Evidence map›Paper›PMID 40138421›Full record

ArticleScience advances2025

Nanoelectronics-enabled reservoir computing hardware for real-time robotic controls.

Mingze Chen, Xiaoqiu An, Seung Jun Ki, Xirong Liu, Nihal Sekhon, Artyom Boyarov, Anushka Acharya, Justin Tawil, Maxwell Bederman, Xiaogan Liang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2025. 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. 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

10 authors.

Mingze ChenDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-4265-1544
Xiaoqiu AnDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Seung Jun KiDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0001-5031-6036
Xirong LiuDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Nihal SekhonDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Artyom BoyarovDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0001-9043-7393
Anushka AcharyaDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Justin TawilDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Maxwell BedermanDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Xiaogan LiangDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-7390-9130

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced control systems based on different device physics are essential for the advancement of sophisticated robotic vehicles and miniature mobile robots. Here, we present a nanoelectronics-enabled analog control system mimicking conventional controllers' dynamic responses for real-time robotic controls, substantially reducing training cost, power consumption, and footprint. This system uses a reservoir computing network with interconnected memristive channels made from layered semiconductors. The network's nonlinear switching and short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling the generation of motor control signals with a simply trained readout layer. This approach minimizes software and analog-to-digital conversions, enhancing energy and resource efficiency. We demonstrate this system with two control tasks: rover target tracking and drone lever balancing, achieving similar performance to traditional controllers with ~10-microwatt power consumption. This work paves the way for ultralow-power edge computing in miniature robotic systems.

Identifiers

PMID40138421
PMCPMC11939057

What OpenQuestion 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 OpenQuestion graph.