Over the past decade, the robotics industry has undergone a profound evolution, propelled by advances in artificial intelligence, knowledge representation, and memory architectures. The integration of knowledge graphs and ontological memory into robotics has unlocked new possibilities, allowing machines to reason, adapt, and interact in increasingly sophisticated ways. This article profiles 25 global robotics startups at the forefront of this movement, with a special focus on how they leverage structured knowledge to drive innovation. Alongside, we examine their funding backgrounds, technology stacks, and real-world applications.
Emerging Leaders in Robotics Knowledge Representation
Knowledge graphs and ontological memory have become crucial for robots to interpret and interact with the world in a human-like fashion. These technologies allow robots to store, retrieve, and reason about complex relationships — not only data, but context and meaning. Below, we highlight several startups that exemplify this trend.
1. Covariant (United States)
Funding: Over $220 million (Series C, 2023)
Tech Stack: Python, PyTorch, Neo4j, ROS
Use Case: Warehouse automation with AI-powered picking robots leveraging deep learning and knowledge graphs for object recognition and manipulation. Covariant’s system integrates ontological memory to generalize across novel objects and tasks, continually improving through experience.
2. Blue Ocean Robotics (Denmark)
Funding: $66 million (2020)
Tech Stack: C++, ROS, custom ontological DB
Use Case: Healthcare and service robots, including UV disinfection bots. Knowledge graphs model environments and tasks, enabling flexible adaptation and safe navigation in dynamic hospital settings.
3. Rapyuta Robotics (Japan/Switzerland)
Funding: $51 million (2022)
Tech Stack: Cloud robotics, Knowledge Graphs (RDF), Python, ROS
Use Case: Logistics robots with shared cloud-based ontological memory, enabling collaborative navigation and persistent understanding of warehouse layouts and inventory changes.
4. Kindred (acquired by Ocado) (Canada/UK)
Funding: $92 million (pre-acquisition)
Tech Stack: PyTorch, Knowledge Graphs, ROS
Use Case: AI-powered picking robots in e-commerce fulfillment centers, using ontological knowledge to distinguish and manipulate a wide array of items with high accuracy.
5. Agility Robotics (United States)
Funding: $180 million (2023)
Tech Stack: ROS, C++, Prolog-based ontologies
Use Case: Mobile bipedal robots for logistics and delivery, using ontological memory for scene understanding and real-time decision-making in complex human environments.
Enabling Contextual and Semantic Reasoning
The application of knowledge graphs and ontological memory goes far beyond navigation or object recognition. These frameworks enable robots to understand intent, context, and even social cues, supporting richer human-robot interaction.
6. PAL Robotics (Spain)
Funding: Undisclosed (est. $20-30 million)
Tech Stack: ROS, OWL Ontologies, Python
Use Case: Humanoid robots for assistive care and public spaces, employing ontological memory to interpret human commands in natural language, maintain contextual awareness, and provide personalized assistance.
7. Roboy (Germany/Switzerland)
Funding: Public and private grants (est. $10 million)
Tech Stack: Neo4j, ROS, Python
Use Case: Human-like robots with social interaction capabilities, using knowledge graphs to manage and recall information about users, preferences, and conversational history.
“Robots that understand context can build trust and forge lasting partnerships with humans, not just perform tasks.”
8. Fetch Robotics (acquired by Zebra Technologies) (United States)
Funding: $94 million (prior to acquisition)
Tech Stack: ROS, Knowledge Graphs, C++
Use Case: Autonomous mobile robots in logistics, using knowledge graphs to optimize routes and manage inventory relationships in real time.
9. GreyOrange (India/Singapore/US)
Funding: $170 million (2022)
Tech Stack: Java, Neo4j, ROS
Use Case: AI-enabled warehouse robots, leveraging ontological models for dynamic task allocation and efficient resource management.
10. Bright Machines (US/Israel)
Funding: $352 million (2023)
Tech Stack: Python, RDF, Docker, ROS
Use Case: Manufacturing automation, using knowledge graphs to model assembly processes, component relationships, and production workflows.
Scaling Knowledge: Shared Ontologies Across Fleets
As robotics deployments grow in scale, knowledge sharing becomes a key differentiator. Startups are building platforms where fleets of robots learn collectively, updating and synchronizing their ontological memory through cloud-based knowledge graphs.
11. Formant (United States)
Funding: $28 million (2023)
Tech Stack: Node.js, MongoDB, GraphQL, RDF
Use Case: Cloud robotics management platform, allowing heterogeneous fleets to share and update knowledge graphs, supporting adaptive, data-driven operations across sites.
12. Pangolin Robotics (Singapore)
Funding: $10 million (2022)
Tech Stack: ROS, Neo4j, Python
Use Case: Service robots in hospitality, using ontological memory to learn guest preferences and optimize task scheduling across multiple robots.
13. Starship Technologies (UK/Estonia/US)
Funding: $197 million (2023)
Tech Stack: C++, ROS, Graph Databases
Use Case: Autonomous delivery robots, employing knowledge graphs for environmental modeling, path planning, and experience sharing between units.
14. Magazino (Germany)
Funding: $50 million (2022)
Tech Stack: Python, ROS, Knowledge Graphs
Use Case: Intelligent picking robots for logistics, leveraging dynamic ontological memory to adapt to changing warehouse inventories and layouts.
15. SESTO Robotics (Singapore)
Funding: $5 million (2021)
Tech Stack: ROS, Neo4j, C++
Use Case: Autonomous mobile robots for healthcare and industry, sharing semantic maps and task ontologies to coordinate complex workflows between multiple units.
Semantic AI for Robotics: New Frontiers
Beyond industrial and logistics robotics, several startups are pioneering the use of knowledge graphs in areas such as home assistance, agriculture, and exploration. Their work hints at how ontological memory might shape the next generation of adaptable, context-aware robots.
16. Temi (Robotemi) (Israel/US)
Funding: $82 million (2023)
Tech Stack: Android, Java, Graph Databases
Use Case: Personal assistant robots, using knowledge graphs to manage user profiles, routines, and contextual reminders.
17. Iron Ox (United States)
Funding: $98 million (2021)
Tech Stack: Python, Neo4j, ROS
Use Case: Autonomous farming robots, employing ontological models to track plant health, soil data, and optimize resource distribution across greenhouses.
18. Wyca Robotics (France)
Funding: $5 million (2022)
Tech Stack: ROS, RDF, Python
Use Case: Indoor service robots, utilizing semantic knowledge for dynamic mapping and contextual task execution in retail and public spaces.
19. FarmWise (United States)
Funding: $65 million (2022)
Tech Stack: ROS, Prolog, Knowledge Graphs
Use Case: Agricultural robots for precision weeding, leveraging ontological memory to distinguish crops from weeds and adapt strategies over time.
20. Ubiquity Robotics (United States)
Funding: Undisclosed (seed stage)
Tech Stack: ROS, Python, RDF
Use Case: General-purpose mobile robots with open-source ontological frameworks for navigation, object detection, and task planning.
Rising Innovators: Niche and High-Impact Applications
While some startups target broad markets, others are carving out specialized niches, using knowledge graphs and ontological memory to solve problems in inspection, eldercare, education, and more.
21. Shadow Robot Company (UK)
Funding: Private, estimated $10 million
Tech Stack: ROS, Neo4j, Python
Use Case: Dexterous robotic hands, utilizing knowledge graphs to plan grasp strategies and learn from teleoperation sessions.
22. F&P Robotics (Switzerland)
Funding: $5 million (2022)
Tech Stack: ROS, RDF, Python
Use Case: Assistive robots for elderly care, applying ontological memory to personalize routines, recognize medical needs, and interact with caregivers.
23. LuxAI (Luxembourg)
Funding: $3 million (2021)
Tech Stack: Python, ROS, Knowledge Graphs
Use Case: Social robots for autism therapy and learning, employing semantic memory to adapt educational content to individual progress.
24. Robot Perception Lab (Spin-off: Roboception) (Germany)
Funding: $4 million (2022)
Tech Stack: C++, ROS, Graph Databases
Use Case: 3D perception systems for industrial robots, using ontological frameworks to interpret complex scenes and guide manipulation tasks.
25. Deepfield Robotics (Bosch) (Germany)
Funding: Corporate-backed
Tech Stack: ROS, Python, RDF, Neo4j
Use Case: Agricultural field robots, leveraging knowledge graphs to interpret environmental data and coordinate multi-robot teams for crop management.
The Future of Knowledge-Driven Robotics
The startups profiled here are not only advancing the state of the art in robotics, but also redefining how machines “know” and “remember.” Knowledge graphs and ontological memory are proving indispensable for robots that must operate in unstructured, unpredictable environments, collaborate with humans, and adapt over time.
Technically, these startups demonstrate a remarkable diversity in their approach: some build atop open-source frameworks, others develop proprietary ontological models, and many combine both with advanced perception and natural language technologies. The rapid adoption of graph databases such as Neo4j, semantic web standards like RDF and OWL, and the integration of these with real-time robotics platforms (notably ROS), marks a convergence of AI, robotics, and knowledge engineering.
Commercially, the sector has attracted billions in venture funding, with logistics and manufacturing leading the way, but strong growth is also seen in agriculture, healthcare, and personal robotics. The use cases range from warehouses and farms to hospitals and homes, each benefiting from the robot’s ability to contextualize, reason, and learn from experience.
“The promise of knowledge-driven robotics is not just efficiency, but new forms of collaboration, creativity, and care.”
The trajectory of these 25 startups signals a future where robots are not mere automatons, but partners endowed with semantic understanding, capable of navigating the complexities of our world with intelligence and empathy. The journey is ongoing, and each advance in ontological memory brings us closer to truly intelligent machines.
