Robot Learning Patent Landscape 2026
Robot Learning Patent Landscape in 2026
The robot learning patent field is concentrated at the top, with the five largest filers accounting for 41% of the hundred largest filers’ combined total, led by Strong Force VCN Portfolio 2019 LLC. Annual volume peaked in 2021 and has since eased, though the multi-year window still reflects positive growth.
Strong Force VCN leads a moderately concentrated field
Strong Force VCN Portfolio 2019 LLC holds the top position with 70 patent families, followed by Brain Corp at 55 and Robert Bosch GmbH at 51. These three entities form a distinct leading tier well ahead of the rest of the ranked field.
The top five filers collectively account for 41% of the hundred largest filers’ combined total — a level of concentration that signals meaningful but not impenetrable incumbency. A gap separates the top three from Rethink Robotics (22 families) and GDM Holding (16 families), defining a clear second tier.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | Strong Force VCN Portfolio 2019 LLC | 70 | |
| 2 | Brain Corp | 55 | |
| 3 | Robert Bosch GmbH | 51 | |
| 4 | Rethink Robotics Inc | 22 | |
| 5 | GDM Holding LLC | 16 | |
| 6 | Willand (Beijing) Technology Co Ltd | 15 | |
| 7 | Omron Corporation | 13 | |
| 8 | Strong Force TX Portfolio 2018 LLC | 11 | |
| 9 | Five AI Ltd | 10 | |
| 10 | Mitsubishi Electric Research Laboratories Inc | 10 |
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 11 | Genmark Automation Inc | 10 | |
| 12 | Samsung Electronics Co Ltd | 10 | |
| 13 | Mitsubishi Electric Corporation | 9 | |
| 14 | Honda Motor Co Ltd | 9 | |
| 15 | Toyota Motor Corporation | 8 | |
| 16 | Collaborative Robotics | 8 | |
| 17 | Acumino | 8 | |
| 18 | Huawei Technologies Co Ltd | 8 | |
| 19 | Google LLC | 7 | |
| 20 | Aurora Operations Inc | 6 |
The leaders’ positions reflect different strategic postures: Strong Force VCN is a portfolio vehicle with broad coverage across fleet management and business-process automation, Brain Corp is focused on autonomous navigation and deep-learning control, and Bosch brings integrated industrial robotics expertise. This diversity at the top means no single technical approach dominates.
Filing counts for 2024–2026 are subject to publication lag and should be treated as underestimates; the apparent dip in those years does not necessarily reflect a real slowdown. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
A 2021 filing peak followed by plateau, with manipulator and AI classes dominant
Two views reveal the field’s trajectory and technical breadth: the annual filing curve shows where activity has been concentrated over time, and the IPC class breakdown shows which technology branches absorb the most attention.
Annual filing trend
Filings climbed from 43 in 2017 to a peak of 89 in 2021, then eased to 75 in 2022 and 69 in 2023. The 2024 and 2025 figures are almost certainly under-counted due to publication lag and should not be read as a real contraction. The multi-year growth rate of 9% confirms the field is still expanding on a cumulative basis, even as annual volume has eased from the 2021 peak.
↗ Hover for values · click a bar to ask EurekaTechnology composition
B25J (Manipulators & robots) is the dominant class by a wide margin, followed by G06N (AI computing models), G05D (control of non-electric variables), and G05B (control and regulating systems). Together these four classes capture the core hardware-software integration theme of robot learning. Secondary classes such as G06F, G06Q, G06V, and H04L signal growing interest in data processing, business-logic integration, vision, and communications — each representing a smaller but non-trivial share of activity.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
Method, recording medium, and system for optimally…
A method for optimally generating robot learning data using an AI model, includes: collecting and representing diverse data related to a robot’s task and environment, integrating the data using a multimodal AI model, retrieving relevant information from a knowledge base using a Retrieval Augmented Generation (RAG) framework, generating robot learning data… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | System, method and apparatus for organizing groups… | 323 |
| 2 | Robot Fleet Management for Value Chain Networks | 322 |
| 3 | Job Parsing in Robot Fleet Resource Configuration | 311 |
| 4 | Apparatus and methods for control of robot actions… | 280 |
| 5 | Navigation system for a mobile robot | 272 |
| 6 | Apparatus and methods for control of robot actions… | 263 |
| 7 | Learning system and method for optimizing control … | 227 |
| 8 | Adaptive predictor apparatus and methods | 190 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
What the competitive structure means for R&D strategy
Four structural observations — maturity stage, concentration, collaboration patterns, and geographic spread — translate directly into investment priorities for teams entering or expanding in robot learning.
Field approaching maturity with annual volume easing from its 2021 peak
The lifecycle evidence places robot learning at a Maturity stage: annual filings plateaued near their 2021 high and have not resumed sustained growth. For R&D teams, this implies that broad, undifferentiated robot learning claims are increasingly contested. Investment should target specific technical sub-problems — such as learning from demonstration, sim-to-real transfer, or continual learning — where differentiation is still achievable.
Maturity stageTop three filers hold a decisive lead; mid-tier remains accessible
Strong Force VCN (70 families), Brain Corp (55), and Bosch (51) form a leading cluster that is roughly twice the size of the next ranked applicant. However, with 41% top-five share among the hundred largest filers, the field is not monopolised. A mid-tier of roughly 10–15 filers with 8–22 families each — including Omron, Samsung, Mitsubishi Electric, and Google — demonstrates that challengers can build meaningful positions. New entrants should plan for at least a three-to-five year filing programme to establish defensible coverage.
Moderate concentrationAcademic-industry co-filing is nascent but present
The most active co-filing pair on record is Omron Corporation with Keio University (2 joint families), with a second pair of Robert Bosch GmbH and TU Darmstadt (1 joint family). The low absolute numbers indicate that formal co-invention between industry and academia is not yet a dominant channel in this field, leaving room for organisations to establish differentiated academic partnerships as a strategic lever.
Early-stage collaborationUS-centric filing with China and Europe as meaningful secondary markets
The United States is the lead filing jurisdiction by a substantial margin, followed by China, the European Patent Office, and WIPO PCT. Germany and Japan appear as national-office destinations reflecting the manufacturing-robot heritage of Bosch, Mitsubishi Electric, and Kawasaki. Coverage in South Korea, Canada, and Australia is present but thin. Teams commercialising robot learning outside the US should assess whether their China and EPO filings are sufficient given the growing industrial-robot activity in those regions.
US-led, global reachGo beyond the landscape: Eureka’s TRIZ Solution agent breaks down an R&D problem and returns patented concept solutions, each with a technical approach and cited patent & literature evidence.
| Applicant | Collaborator | Co-filings |
|---|---|---|
| Omron Corporation | Keio University | 2 |
| Robert Bosch GmbH | TU Darmstadt (Technische Universität Darmstadt) | 1 |
Co-filing pairs, ranked by the number of jointly-filed patent families.
Strong Force VCN and Brain Corp lead; Bosch is the strongest industrial incumbent
The leading patent families are held by two portfolio entities and one large industrial manufacturer, each with distinct technology emphases. Momentum data shows Sharp growth from new entrants alongside a steep decline for the prior autonomous-navigation leader.
Strong Force VCN Portfolio 2019 LLC
Holds 70 patent families — the largest portfolio in the field — with primary coverage across business and administrative data processing (G06Q), manipulator and robot hardware (B25J), and autonomous navigation control (G05D). The momentum data marks it as a new entrant in the recent window, with 64 of its families filed in that period, indicating this position was assembled rapidly rather than grown organically over many years. This profile is consistent with an IP aggregation strategy targeting fleet management and value-chain automation.
families: 70Robert Bosch GmbH
Holds 51 patent families, ranking third overall, with a technology emphasis concentrated in manipulator systems (B25J subclasses 9 and 13) and neural-network AI models (G06N). Recent-window momentum shows a 35% decline versus the prior period, suggesting Bosch has moderated its filing pace after an earlier build-up phase — a pattern common among large industrials consolidating rather than expanding coverage. Its engineering-led portfolio is more organically developed than the portfolio-entity leaders and therefore likely to reflect deeper implementation detail.
families: 51| Applicant | Recent (3 yrs) | Trend |
|---|---|---|
| Strong Force VCN Portfolio 2019 LLC | 64 | ▲ new entrant |
| Brain Corp | 1 | ▼ -93% |
| Robert Bosch GmbH | 20 | ▼ -35% |
| X Development LLC | 2 | ▲ new entrant |
| Willand (Beijing) Technology Co Ltd | 14 | ▲ new entrant |
| Omron Corporation | 4 | ▲ new entrant |
| Strong Force TX Portfolio 2018 LLC | 4 | ▲ new entrant |
Under-served adjacent branches worth monitoring
Several IPC classes appear at the periphery of the robot learning corpus with relatively low patent-record counts relative to the dominant B25J and G06N classes. These represent observed sparsity; technical plausibility and entry feasibility must be assessed case by case.
G06V · Image and Video Recognition
With 50 patent records against 511 for B25J, visual perception is under-represented given its practical importance to robot learning — particularly for manipulation, inspection, and human-robot interaction tasks. The technical link between learned visual representations and robot policy training is well established, and existing applicants such as Rethink Robotics (which already indexes G06V subclasses) demonstrate feasibility. An entrant with strong computer-vision IP could differentiate by bridging G06V and G06N within a robot-learning context.
Search this in Eureka →H04L · Digital Information Transmission
Only 36 patent records touch H04L, which covers the communication infrastructure needed for multi-robot coordination, cloud-based learning updates, and edge-inference pipelines. As robot fleets scale, low-latency and secure data transmission becomes a bottleneck in learning system deployment. This branch is sparse, has clear technical relevance to fleet-level robot learning, and is adjacent to the G05D and G06Q work already prominent in the corpus — making it a plausible area for teams working on distributed or federated robot learning architectures.
Search this in Eureka →How leading filers differ across technology routes
Strength of each leader across the main technology routes.
| Player | B25J 9 · Manipulators & robots | G05D 1 · Control of non-electric variables | G06N 3 · Computing based on AI models | G06N 20 · Computing based on AI models | G05B 19 · Control & regulating systems |
|---|---|---|---|---|---|
| Strong Force VCN Portfolio 2019 LLC | Strong · 51 | Strong · 37 | Moderate · 24 | Strong · 30 | Strong · 29 |
| Brain Corp | Strong · 41 | Strong · 29 | Strong · 27 | Moderate · 17 | Moderate · 9 |
| Robert Bosch GmbH | Strong · 42 | Emerging · 6 | Moderate · 10 | Moderate · 9 | Absent |
| Rethink Robotics Inc | Strong · 21 | Absent | Absent | Strong · 21 | Strong · 14 |
| Strong Force TX Portfolio 2018 LLC | Strong · 10 | Absent | Strong · 9 | Strong · 9 | Strong · 10 |
| X Development LLC | Strong · 16 | Absent | Moderate · 4 | Strong · 9 | Absent |
Frequently asked questions
The analysis covers 506 patent families in scope globally. Filing counts for the most recent 18–24 months are subject to publication lag and should be treated as underestimates.
Strong Force VCN Portfolio 2019 LLC holds the largest position with 70 patent families, followed by Brain Corp with 55 and Robert Bosch GmbH with 51.
The field reached a filing peak in 2021 with 89 families and has since eased. The multi-year growth rate remains positive at 9%, placing the field at a Maturity stage where annual volume has plateaued near its peak rather than continuing to climb.
B25J (Manipulators & robots) is the dominant class at 511 patent records, followed by G06N (AI computing models) at 224, G05D (control of non-electric variables) at 157, and G05B (control and regulating systems) at 143. These four classes capture the core hardware-software integration theme.
The United States is the lead filing office with 293 patent records, followed by China at 77, the European Patent Office at 70, and WIPO PCT at 62. Germany and Japan are the next most active national offices.
Co-filing activity is limited in the available evidence. The most active pairing is Omron Corporation with Keio University at 2 joint families, and Robert Bosch GmbH with TU Darmstadt at 1 joint family. Formal co-invention between industry and academia is not yet a dominant channel in this field.
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Disclaimer. This page is generated from PatSnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
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