Robotics technology is advancing rapidly, but development speed is hampered as teams repeatedly build basic infrastructure, like data pipelines, from scratch.
Imagine walking into a kitchen to learn how to cook, only to find no stores selling knives, cutting boards, or stoves, forcing the chef to forge their own blades and carve their own boards. You would spend far more time building tools than actually cooking. This is precisely the situation the robotics industry faces today. Teams building robots are repeatedly creating the “foundational infrastructure” (the plumbing) for collecting and processing data from the ground up. Source 1 Source 6
Why does this matter?
Robots are no longer just simple machines; they are evolving into “Embodied AI,” combined with artificial intelligence. However, the data systems essential for these robots to gain intelligence are not standardized. The fact that robotics teams pour valuable time into infrastructure construction means their speed in experimenting with innovative technologies or bringing products to market is slowed down. Source 8 We want to see smarter robots sooner, but the people making them are tied up in the business of making kitchen utensils.
Easy explanation: Why don’t web-era tools work?
A “Data Stack” is a type of “digital warehouse” system that stores and manages information collected by robots. The web-based data tools we have used until now were optimized for processing things like click counts or order information on the internet. Source 7 But robots are different.
Let’s use an analogy: if web data is information focused on “text,” robotics data is “moving video and physical sensations.” If a web-era tool is an office that classifies “letters,” the system required by a robot needs to be a high-speed film studio capable of “synchronizing high-definition video captured by thousands of cameras simultaneously with pressure data felt by a robotic arm.” Source 7 Existing tools fall short of capturing the fidelity (how similar it is to real data) of the minute and massive physical data robots encounter in the field. Source 4
Furthermore, while internet text data can be “scraped” from websites, robotics data is different. Robots must directly collide and interact with the real world to collect data stitch by stitch. Source 9 Consequently, it is not easy to borrow data created by other teams, and teams end up repeating the struggle of building from scratch every time. Source 9
Current status: The full-stack struggle
Because of these difficulties, many robotics teams are choosing a “full-stack” strategy, building everything from beginning to end themselves. Source 2 Since the brain (AI model) responsible for intelligence and the body (physical robot) are both advancing rapidly, they determine that directly controlling the feedback process between the two without relying on others is the way to win in the competition. Source 2
However, as mentioned earlier, this incurs tremendous human and temporal costs. Teams are pouring effort into repeating the same tasks, such as data pipelines, synchronization systems, and log recording methods. Source 5 While there is a strong call for better architectures and metrics to integrate and manage data in the enterprise AI sector, Source 4 the robotics field is still in an early stage where even a “common data set” for robots has not been established. Source 9
What will happen in the future?
Fortunately, there are signs of change. Recently, many companies and researchers are working to create a new common infrastructure layer that helps robotics developers focus on “real robot intelligence” rather than “infrastructure plumbing.” Source 6 If they establish standards for robotics data and build a public system that anyone can easily use, robotics teams will finally be free from the shackles of tool creation. Source 1 Source 5
For robots to become smarter faster, we must first improve the environment that forces robotics engineers to become “tool makers” rather than chefs. We should watch how the data stack in the robotics field evolves beyond web-era methods into a form optimized for robots.
References
- RoboticsTeamsAreRebuildingtheDataStackfromScratch
- More and more robotics teams are going full stack
- What I Learned About Robotics in 72 Hours
- Rebuilding the data stack for AI - MIT Technology Review
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[Ep 97 Why Robotics Keeps Rebuilding the Same Infrastructure](https://www.22astronauts.com/p/ep-97-why-robotics-keeps-rebuilding-036) - Backing Neuracore: Reinventing Data Infrastructure for Robotics
- Rebuilding the Data Stack for AI: Web-Era Systems Can’t Keep Up
- How Neuracore solves robotics infrastructure woes
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[The data gap that’s holding back robotics IBM](https://www.ibm.com/think/news/the-data-gap-holding-back-robotics) - Data Centers Are Expanding — Will Operators Turn to Robots for Management?
- Tools from the web era cannot meet the high accuracy and quality requirements of robotics data
- Existing tools are too expensive
- Every team wants their own unique data format
- The sheer volume of data is overwhelming
- It can only be obtained through physical interaction
- It can be easily scraped from the internet
- Their teams are too small
- To directly control the feedback loop as the intelligence layer and physical platform evolve
- To save on infrastructure building costs