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AquiaRobotics

Story 04 · 12 October 2026

What do companies study in robotics?

Google, BMW and China Southern Power Grid appear in the robotics papers we collected. Their researchers work on different problems, from learning and driving to inspecting power lines. We open a few of those papers, then look at the larger patterns of company participation in the research.

Field of viewThe papers we gathered by following citations, grouped by those citations into research topics, and the institutions their authors name. A company author shows a company taking part in research, not a product; only papers whose authors name an institution can be counted, and the share that do is stated on every figure.How much can this map see?

Measured

01 · One paper

What does a power company have to do with robots?

One paper

Research of Multi-Rotor UAVs Detailed Autonomous Inspection Technology of Transmission Lines Based on Route Planning

2019 · China Southern Power Grid

Cited 81 times, as OpenAlex counts; one of the 3,853 papers we file under inspecting power lines.

The company as OpenAlex lists it: China Southern Power Grid (China).

One paper from the collection, as its row prints it: the title, the year, the company among its authors’ institutions and OpenAlex’s citation count. The words for the paper’s problem are ours, read from its abstract.Measured

In 2019 engineers at China Southern Power Grid, one of China’s two grid operators, published a paper on a chore that sounds nothing like robotics: inspecting high-voltage transmission towers. The paper works out safety rules and flight routes for a small drone to fly the tower and photograph it automatically, tower after tower. That is a robotics paper by any measure we use: it sits among 3,853 papers that cite one another on the same problem, which we file as one research topic. It has been cited 81 times. And its authors work for a power company, not a university or a robot maker. This story asks how often that happens, and where.

02 · Three problems

Who publishes about each problem?

drivingDaimler (Germany)413Robert Bosch (Germany)289Ford Motor Company(United States)255Volvo (Sweden)213BMW Group (Germany)212learningGoogle (United States)806Meta (Israel)203Microsoft (UnitedStates)174Microsoft Research(United Kingdom)164Intel (United States)91inspecting power linesChina Southern PowerGrid (China)167State Grid Corporationof China (China)132Shanghai Electric(China)82Inner Mongolia ElectricPower (China)29Power Grid Corporation(India)18
For each of three research topics, the five company institutions with the most papers in it, as OpenAlex names them (OpenAlex lists BMW Group and BMW apart, and only BMW Group reaches the five). Papers 2005–2024; only papers whose authors name an institution count. Each panel has its own scale from zero; the number beside a bar is its count.Measured
Data table
TopicInstitution, as OpenAlex names itpapers with a company author naming the institution
drivingDaimler (Germany)413
drivingRobert Bosch (Germany)289
drivingFord Motor Company (United States)255
drivingVolvo (Sweden)213
drivingBMW Group (Germany)212
learningGoogle (United States)806
learningMeta (Israel)203
learningMicrosoft (United States)174
learningMicrosoft Research (United Kingdom)164
learningIntel (United States)91
inspecting power linesChina Southern Power Grid (China)167
inspecting power linesState Grid Corporation of China (China)132
inspecting power linesShanghai Electric (China)82
inspecting power linesInner Mongolia Electric Power (China)29
inspecting power linesPower Grid Corporation (India)18

Three problems, three kinds of company. Driving — 78,776 papers on how a car sees the road and decides — is where the car industry publishes: Daimler (Germany), Robert Bosch (Germany), Ford Motor Company (United States). Learning — 55,019 papers on how a machine improves by trial — is the technology companies’: Google (United States) first by a distance, then Meta (Israel). Inspecting power lines — 3,853 papers — is the utilities’: China Southern Power Grid (China) and State Grid Corporation of China (China). Two denominators, and they answer different questions. The bars count papers in a topic that name a company; they do not say how a company spreads its papers across topics.

03 · The pattern

How much of each problem’s research involves a company?

010202005201020152020yearpapers with a company author, % of those naming an institutioninspecting power linesdrivinglearningevery placed topic
One line per topic, by year: of the papers whose authors name an institution, the share with at least one company among them; the ash line is every placed paper. Papers with no affiliation (90.2% carry one) are left out, so a year with thin metadata can move a line.Measured
Data table
yearinspecting power linesdrivinglearningevery placed topic
200513.617.63.07.4
200612.117.94.56.9
20077.116.93.17.0
200812.816.13.86.9
200914.513.74.06.5
201015.114.33.66.2
201111.512.13.45.7
201213.313.33.46.1
201312.612.75.66.1
201413.413.34.75.8
201515.513.85.35.8
20165.812.38.86.0
201715.512.49.15.9
201816.612.114.76.0
201914.713.216.46.6
202017.113.316.46.6
202122.811.714.76.4
202224.411.09.95.9
202323.99.98.65.9
202425.510.47.26.0

Over the last ten years, 11.7% of the driving papers we can attribute carry a company author, 11.9% of the learning papers, and 20.9% of the power-line papers — against 6.1% across every topic we place. The average is small because most robotics research is academic; inside it, three very different stories run. What the lines do not say: a company author is a company taking part in research, not a company using the technique, and not a product. And a line can move for a dull reason — the share of papers whose authors name any institution differs by topic and by year — which is why the caption states it.

Where to go next

Sources

  1. One paper, quoted from its row in the collection — companies-example.json: for each topic the most cited paper with a company author and an abstract, and for inspecting power lines the most cited such paper whose company is an electricity company (a name carrying Power, Grid or Electric), each quoted from its row of the story inputs published on 2026-09-24 (paper_topic_extract) with OpenAlex’s citation count from the papers table. The paper on the screen is the power company’s; the prose reads its abstract.
  2. Three topics and the companies in them, from the story inputs published on 2026-09-24 — companies-by-topic.json: for driving, learning and inspecting power lines, the papers of 2005–2024, those whose authors name an institution and those naming a company, from paper_affiliation_coverage and checked against paper_topic_extract; the company shares over 2015–2024; and the five company institutions with the most papers in each topic, as OpenAlex names them, the record named RoboCat left out as a model rather than an institution.
  3. Company authorship by year and topic, from the same story inputs — companies-over-time.json: for each topic and every placed paper, by year from 2005 to 2024, the share of papers whose authors name an institution that have a company among them, beside the share naming any institution at all.