When Workers Program the Robots: No-Code Interfaces for a Humane Automation

What if production workers could program robots themselves? Research conducted at BFH in partnership with Bien-Air Dental SA shows that no-code interfaces can give workers control over collaborative robots, opening a path to affordable, flexible automation for Swiss SMEs.

The automation paradox

For decades, industrial automation has been optimized for speed, precision, and robustness. This works well for mass production, but it fails where Swiss manufacturing is strongest: low-volume, high-mix production. According to the Swiss Manufacturing Survey, labor costs and the shortage of qualified staff are the two main obstacles to manufacturing in Switzerland. Automation could relieve both, yet many tasks remain manual because reprogramming a robot for each new product requires costly external experts. Collaborative robots (cobots) promised to change this with intuitive, no-code interfaces. In practice, however, ease of use came at the price of flexibility: most plug-and-play systems are limited to very simple tasks, which partly explains their low adoption in Swiss industry. Automation can only be agile if the people on the shop floor can reprogram it themselves.

From ease of use to ease of learning

In the CODIMAN project, a research project at BFH dedicated to keeping workers in control of digital manufacturing technologies, we developed a no-code, block-based programming interface built on a robotic skill-based architecture. A robotic skill is a generalized, reusable robotic functionality, such as picking, placing, or pressing. Instead of writing code, users assemble visual blocks representing these skills to build complete tasks. The key design principle is to prioritize ease of learning over mere ease of use: rather than constraining workers to predefined behaviors, the system helps them progressively build the competences needed to adapt, troubleshoot, and extend the automation on their own.

The Prograblock interface: robotic skills are assembled as visual blocks (left and center), with a live 3D view of the robotic cell (right).

This distinction matters for empowerment. A system that is only easy to use keeps workers dependent on experts. A system that is easy to learn turns the robot into an assistive tool under the worker’s control, much like the computer became for office work.

What we learned on the shop floor

Multiple studies conducted in the lab showed that novices can successfully program robotic tasks with block-based interfaces, with high usability scores and strong completion rates. Working with a physical robot rather than a simulation also reduced the participants’ level of anxiety and supported the development of accurate mental models of the system, particularly for complex tasks.

We then validated our approach in real production at Bien-Air Dental SA, a manufacturer of dental instruments in Biel. A first automated task is now actively running in production, and after training workshops, company technicians started reprogramming the robot for new tasks, with support from our team still needed at times. For the company, this is a decisive advantage: new tasks can be automated in-house, without depending on an external integrator, which reduces costs, shortens time to market, and makes one robotic cell profitable across many products.

The studies also revealed a subtle but important insight about autonomy. When something went wrong, workers often preferred to involve technical support rather than intervene themselves. A closer look showed that this was not a lack of capability but deliberate risk assessment in an environment where mistakes are costly. Autonomy in industry does not mean working alone; it means knowing when to act and when to involve others, what we describe as informed interdependence.

Empowerment is socio-technical

The central lesson of this research is that worker empowerment does not emerge from technical innovation alone, but from aligned socio-technical systems. Simplified interfaces are necessary but not sufficient. Role-specific design, organizational trust, learning structures, and the involvement of workers in the development process all shape whether new technology is experienced as a gain in agency or as a loss of control.

From the lab to industry, and back

These results are now being carried forward on two complementary tracks.

On the research side, a seed grant from BFH’s strategic thematic field Humane Digital Transformation supports the project Robotik Worker Empowerment, which prepares a new Innosuisse proposal with industry partners. Together with work psychologists from BFH’s Institute New Work, we are investigating how continuing education, combining hands-on workshops with tutorials, can enable existing employees to autonomously program and operate robotic systems. The goal is to develop people alongside technology, so that companies can build automation competences in-house with the workforce they already have, and workers are enabled to collaborate with robots instead of being replaced by them.

On the transfer side, with the BFH spin-off Auto-Mate Robotics, founded in December 2024, and a BRIDGE Proof of Concept grant funded by the SNSF and Innosuisse, we are developing problem-based tutorials that teach workers the core robotics and programming concepts they need to become autonomous. The system is vendor-agnostic, is already in use at two Swiss companies, and substantially reduces programming time compared to traditional methods.

A tutorial set-up combining a cobot, tangible objects, and tablets; the same environment is also available in
simulation.

By making automation affordable for low-volume production and keeping workers in control of the machines, this approach addresses both sides of the Humane Digital Transformation: it strengthens the competitiveness of Swiss SMEs while improving the quality of industrial work.

Links

Auto-Mate Robotics: https://www.auto-mate-robotics.ch/

Best Paper CHIRA 2023: Blanc, C., Boudry, L., Sonderegger, A., Nembrini, J., Dégallier-Rochat, S. (2023). Empowering Production Workers to Program Robots: A No-Code, Skill-Based Approach. Springer CCIS.

Blanc, C., Jankowski, J., Sonderegger, A., Calinon, S. und Dégallier Rochat, S. (2025). Intuitive Roboterprogrammierung. Barresi, G. und Wischniewski, S. (Hrsg.). Ergonomie in der Robotik: Fortschritte und Innovationen. Springer.