Hottinger Brüel & Kjær (HBK) recently reported a significant roadblock in autonomous system development: reliability. This issue dictates whether a lab’s investment in automation actually scales or simply creates a new maintenance burden. According to the May 2026 report, today's durability models fail to account for unpredictable real-world variables. This gap is especially felt in laboratories, where robots are now transitioning from basic tasks to multifaceted operations.
Redefining robotics reliability for unpredictable environments
Traditional machinery often operates on a fixed schedule with uniform duty cycles. A centrifuge or an incubator, for example, typically experiences predictable wear patterns. According to the HBK paper, robotics do not follow this template. Loads change constantly as tasks shift, and the ways in which human users interact with these systems are rarely consistent.
When a robot moves from handling a single 10-milliliter vial to managing a heavy tray of reagents, the mechanical stress, electronic load, and sensing requirements all shift simultaneously. These "non-uniform duty cycles" make it difficult to maintain early design assumptions once the equipment is deployed in the field.
Furthermore, the paper highlights that failure modes in robotics are increasingly systemic. In many automated systems, a mechanical issue often stems from a sensing error or a control behavior quirk. Because these architectures are so tightly coupled, a failure in one subsystem can propagate across the entire unit. For a lab manager, this means that a seemingly minor sensor drift could eventually lead to a catastrophic mechanical breakdown.
Transitioning to a lifecycle discipline
The core argument presented by HBK is that reliability can no longer be treated as a one-time verification step completed during the procurement phase. Instead, it must become a lifecycle discipline. This approach requires that durability be embedded into the initial design and then continuously monitored through real-time operating data.
As laboratories move toward greater autonomy, the "experimental promise" of a new robotic system must be backed by reliable uptime. If a lab manager cannot trust that a system will perform consistently over thousands of cycles, the perceived efficiency of automation is lost to downtime and troubleshooting.
Organizations that adopt this reliability-focused mindset—viewing durability as a continuous process rather than a checkbox—are better positioned to lead as robotics move from specialized tools to foundational infrastructure.
Strategies for managing automation procurement and uptime
When evaluating new automated systems or robotics, lab managers should look beyond basic performance specifications. The HBK findings suggest several key areas of inquiry for decision-makers:
- Request data on non-uniform duty cycle testing rather than just standard repetitive motion benchmarks
- Inquire about how the vendor monitors interconnected failure modes, particularly between sensors and mechanical actuators
- Prioritize vendors that offer lifecycle monitoring or data-driven maintenance insights
- Evaluate the "systemic" nature of the robot’s architecture to understand how a failure in one component might impact the rest of the unit
By shifting the focus from what a robot can do to how reliably it can do it, lab managers can make more informed purchasing decisions. Investing in systems designed with a lifecycle approach to robotics reliability reduces the long-term total cost of ownership and ensures that automation delivers on its promise of increased productivity.
Ultimately, durability and predictability determine whether a lab can successfully deploy these technologies at scale. As this technology continues to evolve, the ability to maintain uptime will be the true differentiator between a successful automation strategy and a failed experiment.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
