What’s driving the next innovation cycle? In this fireside chat, Aimée Mugisha and Axel Funhoff unpack the key trends, from emerging technologies like solid-state batteries, humanoid robots and AI to macro changes like near-shoring and the retreat from globalization. The session wraps with Axel’s tips for how companies can position themselves for success in the next chapter.

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The workforce shortage isn’t going away

The labor shortage in warehousing and logistics isn’t just a short-term challenge, it’s a structural shift that’s here to stay. Solving it requires more than temporary fixes, and here’s why.

• First of all, the continued growth of e-commerce might be good news for businesses, this increased order volume also means that companies need to invest in innovative staffing solutions and technology to support this growth.
• Secondly, more and more experienced workers are reaching retirement and are not being replaced by younger workers. This challenge could be a unique opportunity to rethink workforce planning as a whole, introducing new tools and processes that make warehouse work less physically demanding and more rewarding, while transferring valuable knowledge to the next generation.
• Lastly, younger generations gravitate more toward tech-driven careers. This trend highlights the importance of reimagining warehouse roles and making them more dynamic, engaging and aligned with the expectations of the modern workforce.

The result is a widening labor gap that can’t be solved by recruitment alone. It calls for a fundamental rethinking of how work gets done—one where technology plays a central role in keeping operations running efficiently.

Redefining work in warehousing and logistics

In response to these persistent labor challenges, many logistics operations are turning to automation, AI and even humanoid systems. Why? Rather than just replacing people, these technologies support the workforce and shift the nature of the work where it’s most critical. By reducing strain, increasing efficiency and improving job quality, they offer long-term benefits that make the investment worthwhile.

Automation and robotics are taking over some of the most physically demanding and repetitive warehouse tasks, such as manual picking, palletizing, sorting, scanning and transporting inventory. These activities are not only labor-intensive but also prone to human error and injury.
This shift creates new, tech-enabled roles focused on oversight and coordination, including:

• Optimizing robotic workflows and performance
• Configuring task assignments to robots
• Troubleshooting and resolving complex system exceptions
• Maintaining and upgrading robotic systems
• Coordinating human-robot collaboration zones

AI and algorithms are transforming warehouse operations by replacing time-consuming, error-prone tasks like manual scheduling, route planning, inventory checks and basic data entry. Algorithms handle these processes faster and more accurately, freeing up human workers to focus on higher-value tasks:

• Analyzing demand patterns to shape inventory strategy
• Collaborating with teams to improve AI models and outputs
• Investigating anomalies and driving continuous process improvements
• Leading AI-enabled workforce planning for peak efficiency
• Training or tagging data sets

While traditional robotics are built for one task in a fixed environment, humanoids can handle variability and complex movements that machines can’t easily replicate. Think of physically demanding jobs, monitoring hazards and assisting during emergencies. They work alongside people, minimizing injury risks and helping with tasks like training, inventory checks and equipment alerts. New jobs focus on:

• Define tasks and policies to balance workloads between operators and humanoids
• Coordinating hybrid teams and humanoid assistants
• Identifying and resolving edge cases
• Tag-teaming stock replenishment
• Providing feedback or data to improve systems

Building a future-ready warehouse workforce

“At MOVU, I worked on these kinds of solutions, allowing warehouses to automate their inventory and reduce the operator workload to a minimum”, says Thomas, Mechanical Design Engineer at Verhaert On-site Consulting. “Automated warehouse solutions are systems that manage the inventory, optimize the operations and improve efficiency and picking accuracy, using different automated storage, retrieval and picking solutions. This dramatically reduces the workforce needed to fulfill all orders, while increasing efficiency and productivity.”

“But the impact goes beyond efficiency”, the on-site consultant continues. “Automation creates opportunities for upskilling and role diversification, enabling workers to take on more technical, engaging responsibilities. This shift makes warehouse roles more attractive, especially to younger generations seeking tech-driven careers, and helps build a more resilient, future-ready workforce.”

Need expert support to implement or develop advanced warehouse systems? Let’s discuss how Verhaert’s on-site consultants can drive innovation within your R&D team!

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1. Automation is no longer optional; it’s the next big leap

Across the halls, one megatrend stood out: the accelerating shift toward hyper-automation. From AI-driven predictive maintenance to high-speed robotic systems, it’s clear that the industry is leaning heavily toward digital tools to boost efficiency and precision. We noted particularly compelling technologies, such as magnetic levitation systems designed to reduce friction and lower energy consumption, that point to real gains in both performance and sustainability.

For organizations navigating these changes, the challenge lies in identifying which technologies offer measurable value and how to implement them effectively within existing operations. It’s a complex landscape, but one that offers real opportunity for those willing to explore, adapt, and invest with purpose.

2. Humanoid robots: hype or hope?

From packaging tasks to performing simple functions in unpredictable environments, humanoid robots were one of the more attention-grabbing developments at this year’s fair. While widespread adoption is likely still several years off, the technology is advancing quickly, and its potential applications are broadening.

Unlike traditional robots optimized for fixed, repetitive tasks, humanoids are being designed for more flexible roles in dynamic settings, such as logistics, agriculture, or construction. They’re not intended to replace existing automation but to fill gaps where adaptability and environmental awareness are required.

For organizations exploring automation strategies, this emerging category raises important questions about ROI, technical feasibility, and integration. While the hype is noticeable, it’s also a moment to start thinking critically about where and when these systems might offer practical value for your organization.

3. Digitization + AI = The real industrial revolution

Digitization is no longer a future ambition, it’s the underlying infrastructure of today’s industrial landscape. From expansive cloud platforms to AI-powered simulation tools, the integration of digital technologies is reshaping how products are designed and manufactured.

This wave of change is more than technology, it’s an organizational shift. It calls for cross-functional collaboration, open innovation, and bold experimentation to bridge digital and physical worlds, whether through AI, software-defined hardware, or integrated engineering platforms. For many, the path forward to connected ecosystems involves navigating complex questions and breaking down siloed thinking.

Rather than viewing digitalization as a one-time investment, it may be more useful to see it as a long-term transformation. One that requires both strategic clarity and operational flexibility.

4. Hydrogen: From hype to hard reality

Hydrogen technologies have clearly moved beyond the prototype phase. At Hannover Messe, we saw practical, scalable solutions – such as Bosch Rexroth’s liquid hydrogen station operating at -270°C and 900 bar – that reflect growing maturity and industrial relevance. Use cases are expanding across mobility, energy infrastructure and industrial systems.

As organizations face mounting pressure to reduce CO₂ emissions and meet evolving climate regulations, hydrogen is emerging as a critical enabler. Not only in energy strategies but also in how products are conceived and developed. For innovators, this presents an opportunity to build hydrogen-ready systems and components that can help customers meet sustainability goals without compromising performance or cost-efficiency.

The key question isn’t just how to adopt hydrogen technologies, but how to embed them into products that give end users a competitive edge. That’s where product innovation plays a central role: turning emerging capabilities into market-ready solutions that solve real problems and create measurable value.

5. Industry 4.0 is evolving, let’s embrace Manufacturing-X

The vision of Industry 4.0 is evolving into something more integrated and ambitious: Manufacturing-X. This emerging framework brings together AI-driven insights, cognitive ergonomics, digital twins, and open data ecosystems to create manufacturing that’s smarter, faster, and more responsive. It’s not just about improving existing processes; it’s about fundamentally rethinking how products are designed, made, and brought to market.

For product innovators, this means embracing greater agility and intelligence throughout the entire product lifecycle, from virtual prototyping to gathering real-time field data. The result? Products that are more adaptive, user-centered, and capable of meeting ever-changing customer demands and market conditions.

The core technologies – smart sensors, robotics, 5G, and integrated IT/OT systems – are already within reach. The real challenge is how to orchestrate them to create products that don’t just function well, but that win in the market. This shift requires a new way of thinking: moving from isolated solutions to designing systems, from automation to intelligence, and from simply efficiency to broader ecosystem thinking.

Innovation with purpose: Seen at Hannover Messe

True innovation isn’t about following the latest trends; it’s about solving real problems with the right tools, at the right time. What stood out at Hannover Messe wasn’t just the technology on display, but the clear direction emerging across industries: companies are focused on finding smarter, more efficient ways to build, operate, and deliver products that truly matter.

From creating cleaner energy systems to integrating AI in manufacturing and rethinking the way digital and physical worlds merge, the opportunity lies in turning complex challenges into competitive advantages. This is where true product and digital innovation happen: developing solutions that not only meet today’s needs but also give your customers a clear edge in a fast-evolving market.

As organizations face new challenges and rising expectations, the key to success will be the ability to innovate with purpose: combining strategy, technology, and user-centric insight to create outcomes that go beyond technical solutions and deliver genuine value. If you’re ready to take the next step in transforming your products and digital capabilities, we’re here to support you. Let’s collaborate to turn your vision into real, impactful solutions that lead the way in tomorrow’s market.

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Why the foundation is cracking

The construction industry has long been the backbone of economic growth, but it now finds itself at a crossroads. External pressures like economic uncertainty, regulatory shifts, and workforce shortages are forcing companies to adapt or risk falling behind. Unlike previous downturns, today’s challenges are structural, requiring long-term strategic solutions rather than short-term fixes.

• Economic uncertainty and inflation have driven up material costs and project financing expenses, putting significant strain on contractors. High interest rates make borrowing more expensive, limiting investment in new projects and slowing industry growth.
• At the same time, changing and often vague regulations complicate project execution. New sustainability and safety standards require companies to adapt, but unclear guidelines create confusion and slow down compliance efforts.
• Perhaps the most pressing issue is the shortage of skilled labor. The European Construction Industry Federation already reported in 2023 that Belgium has 22,000 unfilled construction jobs, Germany 52,000, and the Netherlands 27,000. With an aging workforce and upcoming retirements, the industry faces a critical skills gap.

Despite these difficulties, the industry has an opportunity to turn adversity into progress. Digital innovation can help businesses improve efficiency, streamline operations and services toward their stakeholders and clients, and reduce reliance on scarce labor. Those who invest in new technologies today will shape the future of construction tomorrow.

The digital toolbox of opportunities

While the construction industry faces mounting pressures, innovation is proving to be more than just a way to keep up. It’s becoming an operational differentiator. Many digital technologies that have already transformed other industries can be readily applied to construction, helping companies streamline operations, reduce costs, and build smarter and faster. Let’s go through the digital tools that are reshaping the way buildings are designed and constructed.

To mitigate economic uncertainty and inflation

• AI can use past project data, weather patterns and economic trends for smarter planning and anticipating project delays. Similar predictive solutions already enhance road safety—why not apply them to construction?
• Building information modeling (BIM) enables precise planning of materials, workforce, and equipment, an approach we’ve already implemented for Wienerberger.
• Digital twins allow testing and validating appliance and utility choices based on realistic model simulations.
• By equipping machinery with sensors and predictive maintenance, issues can be detected before breakdowns occur.

To keep up with changing safety & compliance regulations

• AI can streamline permit approvals, automate regulatory checks and predict equipment failures before they disrupt operations.
• AI-powered robots and drones can assess hazardous areas to minimize human exposure to dangerous conditions, while wearable robotics reduce worker fatigue and injury risks.
• Digital twins allow virtual safety drills and risk assessments based on current regulations before construction begins.
• Software platforms can provide real-time documentation, making regulatory audits faster and more accurate. Make sure to check out the platform we helped build for Reynaers.

To address the pressing issue of skilled labor shortages

• AI can predict workforce needs, forecast material demand, automate/optimize resource allocation toward a minimal footprint and prevent supply chain disruptions.
• AI can also capture, collect and share expert knowledge and best practices. For example, we developed a digital platform for Renson that supports installers with product information, tutorials, and troubleshooting.
• A shared digital model ensures all stakeholders, architects, engineers and contractors work efficiently with up-to-date information. Learn more in our webinar on digital twins for buildings.
• Robots can take on physically demanding, repetitive tasks like automated bricklaying, welding and concrete pouring. Technologies that are already being used for prefab car parts, including wiring, could easily be introduced in the construction industry, too.
• Multi-axis robotic arms and 3D printing can create intricate stone carvings, complex concrete forms and custom metal or woodwork that were previously cost-prohibitive with unprecedented detail. Start-ups like Monumental Labs for example are already making this happen.

A future full of possibilities

Digital innovation is not about replacing human expertise but enhancing it. By integrating AI, automation, and IoT into construction, companies can reduce inefficiencies, improve safety, and open new creative possibilities. Rather than sacrificing design for cost and speed, builders and architects can reintroduce intricate details, relying on technology for precision and efficiency. While concerns about high investment costs and implementation challenges remain, the real opportunity lies in applying existing innovations from other industries, like automotive, ship and plane building. With a forward-thinking mindset, the construction sector can embrace these technologies to build faster, more sustainably, and with greater artistic freedom than ever before.

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As robotics become essential to meet labor needs, the demand for precision and fail-proof solutions is skyrocketing. So why aren’t these autonomous solutions getting adopted more quickly? Well, these robotic systems tend to be complex. Tackling ever-changing variables in the use case and environment requires an integrated, customized approach. In this track, we’ll dive into the exciting world of Industry 4.0, exploring how to cut through the complexity and bring these game-changing solutions to market. Faster, smarter and ready to achieve rapid impact. Check out the presentations from the Innovation Day of 2024 on:

• Open innovation to accelerate Industry 4.0
  By Guus Colman, Consultant EmbeddedLab and Klaas Hofman, Consultant EmbeddedLab at Verhaert Product Innovation
• Modular design to make custom robotics accessible
  Thomas Goossens, Mechanical Design Engineer at Verhaert On-site Consulting

View the presentation

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Let’s dive into the diverse avenues of progress in the coffee industry!

1. Intelligent home roasting

Redefining coffee quality and sustainability

Bunafr‘s participation in CES 2024 introduces an intelligent home coffee roaster that transforms the roasting experience. Beyond being a machine, it offers a fully automated, smoke-free process with advanced technologies. The innovation focuses on personalized roasting, sustainability, and direct connections with coffee farmers, promising an unparalleled coffee experience.

Coffee machines: ©Bunafr (left) – ©Midea (right)

2. AI-guided personalization

The future of smart coffee systems

Midea presents the ‘Barista Brew’ coffee brewer/grinder system, a smart coffee solution that leverages AI-guided personalization. With one-touch favorites, BrewIQ AI suggestions and easy tracking, this innovation aims to make coffee brewing accessible to all, regardless of expertise. It sets a new standard for smart coffee systems, emphasizing simplicity and technological advancement.

3. Premium espresso with technological precision

Bosch’s fully automatic machines

Bosch introduces a new line of fully automatic espresso machines, emphasizing premium-quality coffee and technological precision. The Aroma Max System, Active Select Display, and integration with Home Connect® redefine the home coffee experience. This innovation aims to deliver consistent coffee quality, personalization and over 35 beverage options through advanced technology.

Coffee machines: ©FrazyBot (left) – ©Bosch (right)

4. Robotic beverage crafting

The FrazyBot revolution

FrazyBot, a modular beverage brewing robot, redefines at-home drink preparation. Designed to minimize manual interventions and clean-up, the FrazyBot promises precision and professionalism, comparable to café or bar drinks. The Ingredient-Sensing Robotic Technology (InSeRT) ensures an effortless, customizable and high-quality beverage experience at the press of a button.

5. Eco-friendly espresso

Lavazza’s Tiny Eco revolution

Lavazza‘s Tiny Eco emerges as a compact and sustainable espresso machine made from recycled plastic and compostable capsules. With a focus on sustainability and design, this innovation offers an authentic Italian espresso experience. Its easy maintenance, dishwasher-safe components and compatibility with Lavazza capsules contribute to an eco-friendly and responsible coffee-making approach.

Coffee machine ©Lavazza

Comparing the strategies behind the devices

How do these devices and innovation strategies compare to one another? We made the following assessment based on people’s opinions on a coffee lover’s user perception. We used criteria such as emotional evocation, premium feel, personalization, and the social responsibility of the device from a user’s perception.

Notes:

• Emotion addresses: Reflects how the innovation addresses the emotional aspect of the coffee lover.
• Premium feel: Represents the perceived luxury or premium feel associated with the innovation.
• More personalized feeling: Indicates the degree to which the innovation provides a personalized and tailored experience.
• Social responsibility: Reflects the level of commitment to social and environmental responsibility in the innovation.

People however don’t switch their current coffee maker easily, you need a new compelling reason. When looking for innovative features you need to investigate the importance and the satisfaction level of each of the features. That’s why we also added an assessment relative to the willingness to pay.

Notes:

• Satisfaction rating (1-5): Reflects the current satisfaction level with the existing coffee machine.
• Eagerness to Upgrade (1-5): Indicates how interested the potential buyer is in upgrading the coffee machine based on the discussed factors.
• Willingness to Pay Rating (Low-Med-High): Represents the potential buyer’s willingness to pay for an upgraded coffee machine, considering the perceived value of the innovations.

In conclusion

In conclusion, these innovations highlight diverse avenues of innovative progress for the coffee industry, ranging from intelligent roasting and AI-guided personalization to hands-free brewing and eco-friendly espresso solutions. Each emphasizes the unique innovation direction chosen by these respective brands, providing consumers with a glimpse into the transformative landscape of home coffee experiences. However, only 3 concepts stand out in giving enough reasons to switch from their older device to a new coffee maker. This brings me to my final thought: Would a device combining the 3 outstanding strategies (premiumized coffee, robotics and sustainability) be the holy grail of innovation? Or would it more likely fit in the contradiction of less is more?

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Ancient technology with modern-day engineering to tackle the energy crisis

The maritime industry still heavily relies on fossil fuels, which account for a significant portion of its operating costs. Due to the increasing concerns over climate change and the need to reduce costs, there’s a growing demand for energy-saving solutions. This has led to new technologies like drag-reducing air lubrication systems, and the reintegration of renewable energy sources such as wind power, like equipping vessels with inflatable sails, also referred to as wings. However, before investing in any of these technologies, the industry can apply the strategy of considering options and alternatives to evaluate and deselect the least unfeasible and choose cost-effective solutions meeting the right level of risk and innovation level.

Wing sail system © Michelin

Nevertheless, it turns out that the reuse of this ancient technology in combination with modern-day engineering boosts efficiency successfully. Pioneering companies such as Michelin claim that this hybrid technology will be able to reduce fuel costs by 10 to 20% per boat. The visual below show the necessary actions the maritime industry needs to take to meet sustainability needs in the short and long-term future. As a result, the technologies mentioned before could help to reduce the industry’s dependence on fossil fuels, decrease its carbon footprint and optimize efficiency, while also reducing noise pollution.

Sailing towards zero-emission container shipping © KPMG

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Let’s start by clearing out two main misconceptions about the Robot Operating System (ROS). First of all, it’s not an operating system (OS) and it’s not just useful for robots. But since a ‘Not-Only-For-Robots Not-An-Operating-System’ isn’t quite as catchy, let’s refer to it by its original community-accepted name. Secondly, in this blog we’ll only refer to the robotic mechanical devices and NOT to robotic process automation software.

ROS, what?

What is ROS then? It’s actually a middleware. This means that it’s supposed to be placed between software logic and hardware. It’s similar to a real operating system but needs an operating system platform to run on (usually a Linux distribution). It can’t simply run directly on basic hardware. Moreover, devices need an ROS-specific interface to make use of their power, similar to how operating systems need drivers to access hardware resources.

So if it’s not just for robotics, why does it have ‘robot’ in the name? Because it was developed by the robotics community to be used with robots. Even though it can be used with applications that are not robots, most of the development efforts are directed toward perception, navigation and manipulation tasks. This means that highly complex robotic functionalities are already available in the ROS ecosystem.

ROS, how?

Let’s discuss the core power of ROS: nodes and topics. Nodes are somewhat the atomic pieces of software that implement a desired logic, like retrieving sensor data, capturing images, processing input data or sending input to an actuator. Topics are the communication links between nodes, independent entities that nodes can write or read data from – imagine them as lists of messages like internet chat rooms. These simple mechanisms allow the creation of complex distributed and robust architectures, where the overall system isn’t entirely dependent on the individual. As long as the system design allows it, the entire system is sensor and actuator-agnostic. Imagine a random vacuum cleaner robot: the navigation module doesn’t care if the localization uses lidar, ultrasonic or camera data, or if it’s using wheels, tracks or a propeller to move around.

These components should be easily replaceable, the only necessity is the right ROS node to take the hardware input and offer a standardized output on a specific topic (a driver-like piece of software, as we mentioned before). And here’s another very cool thing: each node can be on a separate computing platform or as many nodes as possible can share the same computing platform at the same time. It’s all up to the application architect to decide. The only prerequisite for ROS is to have a special ‘master’ node running that can be accessed by every other node. The drawback is that if the computing platform running the ‘master node’ disconnects from the system, then the entire system fails.

ROS topology example and in an autonomous car application ©VERHAERT

Above you can find an example of such a topology, as well as applied to a simplified autonomous car application. Keep in mind that each one of these nodes can be on the same or different computer as any of the other nodes. You can even deploy this system on multiple autonomous vehicles and then simply add topics that connect multiple vehicles. The limit of the scalability is set only by the computing or networking resources available.

ROS, why?

‘Standardized’ is another keyword for ROS. Its main goal is to offer a base for true rapid robot prototyping. In 2006, Eric and Keeman decided to create a standard framework for all robots (not yet ROS) when they noticed that in software projects there’s little time left to innovate because most of your time is spent simply making the sensors or actuators work with your system or setting up a communication architecture. The ROS is the successor to their initial framework and it fills the shoes perfectly. No more reinventing the wheel. Once a sensor, actuator or algorithm is successfully interfaced by a developer (or a group), it gets an ROS package made available for everyone to use!

Source: Eric and Keenan pitch deck

We’ve mentioned ‘true rapid prototyping’ before, which might have triggered your spider sense if you’ve spent some time between demonstrators, prototypes and products. Some people tend to oversell the former as the latter, but that’s not the case here. ROS is indeed a prototyping system: it’s not robust enough, doesn’t have real-time capabilities and is easily hackable. Moreover, the master node is required to function at all times.

ROS, next?

ROS2, on the other hand, has a completely different low-level implementation of ROS. It’s still based on nodes and topics, but is far from being a prototyping project and aims to be an industry-ready fully-distributed framework. It has more complex robotic libraries for navigation and manipulation that implement state-of-the-art algorithms and is designed so you can’t develop your packages without abiding by proper software engineering rules. It’s now harder to just create ‘something dirty that works for now’.

Other major changes include the ability to encrypt topics and time synchronization is being handled by the Data Distribution Service, a software framework to reliably and securely communicate in real-time. Furthermore, ROS2 doesn’t have a master node, meaning the application can be entirely distributed across multiple devices without having the inherent critical component. Pretty neat I would say.

Let’s reiterate the key aspects of ROS:

1. Core simplicity
2. Standardization of components
3. Native compatibility with distributed systems
4. Industrial readiness (on the way with ROS2)
5. Available for embedded systems (on the way with ROS2)

It’s clear that ROS is a software to watch. Whether you’re developing a fleet of drones, robot vacuum cleaner, robotic manipulation application, coffee machine or smart beer dispenser, make sure to add this awesome open-source framework in your trade-offs!

Hungry for more? Check out our other blog posts to see how ROS connects hardware with AI!

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So far it seems that I have not said anything useful, but that cannot be further from the truth. The key takeaway is that robotics is one of the most interdisciplinary fields of the modern day.  This blog post presents a breakdown into 6 subcomponents of the field of robotics and describes how artificial intelligence (AI) can impact each one of them.

The cycle of action

Let’s imagine a robot, either a basic factory manipulator or Mr. Schwarzenegger’s beloved Sci-Fi character in the famous human-machine interaction cinema series (for the very young readers, that’s the Terminator franchise). If we take a close look at our robot, we can see an advanced powerful mechanical structure. What if we look closer and strip it naked? We can see actuators, motors, cables, and power drives. How about we use a strong lens when looking at the naked machine? We can observe circuit boards and very thin conductive connections. Let’s go to the quantum level now: here we can witness the physical expression of information bits on the very thin embedded connections and inside the microchips.

My eyes hurt from all that focus, so let’s take a few steps back. What if we look at the robot from very far away, what do we see now? A system that exists in and interacts with a physical environment in a safe, efficient, useful and predictable way and is not a sphere in a void (the favorite joke of our physicist friends).

Robotics model ©VERHAERT

To do this the properties or the state of the environment must be made known to our robotic system. This step is called perception. We, humans, use perception all the time through our 5 senses. Next, we need to find a way to use this information and make a decision through reasoning. The reasoning usually outputs a goal. A robotic arm might see an item and reason that it needs to pick it up. To reach this goal one starts planning a series of steps, for instance how the arm should move to pick up the item. To follow this plan, a set of control inputs are required that almost directly translate into actions. Actions that most of the time affect the environment.

Imagine you are working out on a sunny day and see a bottle of water standing on the table. You reason that you are thirsty and should drink the water. First, your brain needs to plan the movement to grab the bottle and bring it towards your mouth. Secondly, you control your muscles in a certain way for each of these steps to perform actions with parts of your body that lead you to reach your goal. Finally, these actions will act on the environment by changing its state. The bottle of water is no longer in the same location and it contains less water than before. The entire perception-reasoning-planning-control-action cycle is never ending for living beings and machines alike.

Demystifying artificial intelligence

How does AI come into play? First of all, what is it? AI is the new buzzword for ‘algorithm’ and is something that engineers say when they do not want to say what they are doing. All jokes aside, an algorithm is a logical set of steps that solve a problem. The same is with an AI system. It’s an implementation of logic that allows said system to detect, learn, adapt, make decisions, and/or perform actions with minimal human intervention. Maybe not a fully accurate, but at least an intuitive definition of an often misunderstood term.

AI algorithms can be classified as either heuristic or learning methods. Heuristic methods usually offer a logic-based solution (or implementation) and are fit for rather simple problems. They don’t require large amounts of data for creating value. On the other hand, learning methods are much more adaptable to complex problems at the cost of sometimes extreme hunger for data. Deep learning is one of the learning methods that exploded in the past decade as hardware capabilities grew exponentially. Therefore, by augmenting the mechanical structure of a robot with AI, we can obtain autonomous machines. Easy.

Getting pragmatic

The story of autonomous machines is a very nice one, but we are not completely there yet. Almost any step of the ‘cycle of life’ as we defined it earlier, can be augmented with AI methods.

• Perception can translate into situational awareness, voice-to-text, natural language processing, computer vision, scene understanding, or chatbots,
• Reasoning will lead to expert systems and goal optimization,
• Planning enables autonomous navigation and distributed task scheduling,
• Action and control is generated with optimal control strategies and reinforcement learning.

The environment itself can be augmented for AI (for example with visual markers), but that is almost cheating and we are trying to develop systems that adapt to their environment, not the other way around. Sometimes a problem can be simplified substantially by limiting the degrees of freedom in the environment. This can make the difference between an impossible task and a simple solution. Think of this like traffic rules: without them, driving would be a much more complex task to handle by any person. This problem is solved by adding rules that should be followed. But we are sliding out of scope again, check other blog posts for more!

One important thing to remember is that adding intelligence to robots is always on the spectrum. The robotic system has to be as intelligent as the complexity of the environment and the task it needs to handle. Assessing this complexity can be an art by itself, but this is what AI experts are good at.

Realistic goals

At Verhaert, we’re aiming to bring robots from ‘hard workers’ to ‘hard and smart co-workers’. From jocks to scholars, the purely physical plane to the cognitive space and, medical applications to space applications. And of course, not to forget about factory automation (automatic quality control, warehouse logistics, predictive maintenance). Not all AI code has to be developed for autonomous Tesla cars, boats deserve an autopilot too. And we are on it.

If you want to know more about the business or product practicality of AI algorithms, this blog is the right place to be. Feel free to check the other posts for how AI can augment your life.

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