Image source: Steve Jurvetson, Wikimedia Commons
Quality assurance is often associated with testing at the end of development. In practice, it is much broader. QA is about building confidence in a system throughout its entire lifecycle, from early design decisions to deployment and long-term operation.
For robotics and AI, this shift is essential. These systems are composed of many interacting components: perception, planning, control, learning, and execution. Each operates under uncertainty, and failures often emerge from their interaction rather than from a single faulty module.
QA helps by introducing structure into this complexity. It encourages explicit assumptions, clear interfaces between components, and systematic monitoring during execution. Instead of asking only “does this work?”, QA asks“ under what conditions does this work, and how do we know when it does not?”
Looking back at my own research, this is where QA could have played a much stronger role. In many cases, we relied on runtime checks and manual inspection to catch issues after something went wrong. A stronger QA approach would introduce guardrails around AI components, verify inputs before execution, and continuously monitor outputs. Problems could be detected earlier, before they turn into failures in the real world.
Learning-based components make quality assurance particularly challenging. AI models often fail in ways that are subtle and difficult to predict. They can produce outputs that look reasonable but are unsafe, inconsistent, or simply wrong in context.
This does not mean these models are unusable. On the contrary, foundation models and learning-based systems can be extremely powerful when treated as components, not oracles. They work best when embedded in a larger system that checks their assumptions, constrains their outputs, and monitors their behavior at runtime.
QA practices such as validation, integration testing, simulation-based verification, and runtime monitoring provide exactly this form of supervision. They allow systems to benefit from powerful AI capabilities without requiring full explainability or perfect predictions.
In robotics, this combination is critical. When AI-generated decisions are directly translated into physical actions, quality assurance becomes a safety mechanism as much as an engineering practice.
Embedded and safety-critical systems change the expectations on quality ina fundamental way. Failures are not just inconvenient; they can be costly or dangerous. Timing constraints, hardware limitations, and interaction with the physical world leave very little room for error.
In these systems, quality cannot be an afterthought. It has to be designed in from the start. QA becomes part of system architecture, not just a final checkpoint before release.
This perspective aligns closely with robotics. Robots are embedded systems by nature. They operate continuously, interact with humans and environments, and must make decisions in real time. In this context, quality assurance is not about slowing innovation down. It is about making advanced systems deployable with confidence.
What excites me most right now is the direction robotics is taking. Quadrupeds and humanoids are becoming more capable, supported by foundation models and increasingly realistic physics-based simulators. The long term promise is clear: systems that can adapt, generalize, and operate in open environments.
At the same time, the limitations are real. Dexterity, long term reliability, safety, and deployment at scale remain hard problems. The current wave of interest will likely settle, but the underlying progress is undeniable.
Foundation models follow a similar trajectory. They are powerful, flexible, and increasingly useful, but they come with limitations such as hallucination and inconsistent behavior. These issues matter when models move from demos to real-world decision-making.
The key risk is not that these models exist, but that they are trusted without sufficient validation, monitoring, and boundaries. When powerful but non-interpretable systems are deployed without strong QA practices, failures become more likely and more dangerous. In high-stakes domains such as healthcare, autonomous systems, or industrial automation, blind trust can have serious consequences.
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(b) Quadruped platform example. Image credit: DEEP Robotics. |
Figure 2: Examples of emerging robotic platforms for domestic and outdoor environments.
This is why I see quality assurance as an enabler rather than a constraint. QA provides the structure that allows advanced AI and robotic systems to be used responsibly. It turns impressive capabilities into dependable systems.
As autonomy increases, quality can no longer be a final gate. It has to be continuous, measurable, and integrated into how systems are designed, built, and operated.
I am optimistic about where robotics and AI are heading. The progress is real, even if the path forward is not simple.
At the same time, optimism without caution would be naive. As systems become more autonomous and less interpretable, the cost of failure increases.We cannot rely on intuition, demos, or blind trust. We need disciplined ways to understand behavior, detect failure, and maintain control.
This is where quality assurance plays a central role. QA is no longer just about testing software before release. It is about continuous confidence in systems that learn, adapt, and act in the real world.
If QA evolves alongside AI, it becomes a key enabler of progress. If it does not, the gap between capability and trust will only grow.
Building intelligent systems is impressive. Building systems we can trust is what will make them truly useful.
Want to read more, here a link to the thesis.