Internet of Robotic Things (IoRT) in Logistics
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Key takeaways
The Internet of Robotic Things connects robots to sensors, networks and software so that machines can share data, coordinate tasks and be monitored as one system.
IoT devices mainly sense and report, while IoRT adds robots that act on that data by moving, picking or carrying goods.
In logistics, IoRT shows up as autonomous mobile robots, goods-to-person systems, automated storage and robotic picking linked to the warehouse management system.
The main challenges are integration between systems from different vendors, cybersecurity, cost, network reliability and the exceptions robots still cannot handle.
Healthcare warehouses can use robotics for picking, inventory accuracy and traceability, but the system must still meet the written warehousing, temperature and quality controls that apply to the products stored.
The Internet of Robotic Things (IoRT) is the point where connected devices and robots meet. Instead of a robot that runs one task on its own, IoRT links robots to sensors, networks, cloud or edge software and to each other, so that the whole operation can be planned, monitored and adjusted in real time. For IoRT in logistics, that usually means warehouse robots that receive work from the warehouse management system, report what they did and share the floor with people and other machines.
This guide explains what the Internet of Robotic Things is, how it differs from the Internet of Things, where it is used in warehouses and distribution, what makes it hard to deploy and how healthcare and life science warehouses can apply it without losing control of quality and compliance.
What is the Internet of Robotic Things?
The Internet of Robotic Things is a network of robots and connected devices that share data with each other and with software platforms, so that robots can sense their surroundings, make or receive decisions and act on the physical world as part of a larger system. The idea is that a robot becomes one more connected "thing" that both produces data and carries out work.
The term has an academic history. A 2016 paper in IEEE Access by Partha Pratim Ray, titled Internet of Robotic Things: Concept, Technologies, and Challenges, set out the architecture, key characteristics and open research problems of IoRT. A 2025 review in the journal Sensors, Internet of Robotic Things: Current Technologies, Challenges, Applications, and Future Research Topics, describes IoRT as the application of IoT to robotic systems and uses a layered model: robots and sensors at the physical layer, a network layer for transmission and an application layer where data is processed, with edge, fog and cloud computing handling work at different distances from the robot.
Put simply, the internet of robotic things concept, technologies and challenges can be summarized in three parts:
Concept. Robots are treated as connected endpoints that send status, location and task data and receive instructions from software or other robots.
Technologies. Sensors and machine vision, wireless networks, edge and cloud computing, warehouse and fleet management software, application programming interfaces and analytics or AI models that turn data into decisions.
Challenges. Interoperability between systems, security of a larger attack surface, latency and network reliability, cost and the many real-world exceptions that software does not anticipate.
How is IoRT different from IoT?
IoT devices mostly sense and report, while IoRT devices sense, decide and then physically act. A temperature logger in a freezer is an IoT device: it measures and sends data, and a person or another system decides what to do. An autonomous mobile robot that receives a pick task, drives to a shelf, avoids a person in the aisle and reports that the tote is ready is part of the Internet of Robotic Things.
The difference matters for how a site plans, secures and maintains the technology. The table compares the three approaches that warehouses usually mix.
Criterion | IoT | Standalone robotics | IoRT |
|---|---|---|---|
Main job | Sense and report conditions or location | Perform a fixed physical task | Sense, coordinate and act as part of a connected system |
Typical warehouse example | Temperature loggers, door sensors, GPS trackers | A fixed conveyor, sorter or robotic arm running its own program | Mobile robots and picking systems that take tasks from the warehouse management system and report back |
Decision making | Usually made by people or central software | Built into the machine's program | Shared between the robot, edge software and central platforms |
Physical risk | Low, the device does not move goods | Contained in a guarded cell or fixed path | Robots move among people and other machines, so safety design is central |
Main integration need | Data feeds into dashboards and alerts | Mechanical and electrical integration | Real-time task, data and safety integration across vendors |
Security impact of a breach | Data loss or false readings | Local disruption | Data loss plus possible physical disruption of operations |
The comparison of IoRT vs IoT shows why a robot project is rarely just an equipment purchase. Once robots depend on networks and shared software, the warehouse inherits the integration and security work that comes with any connected system, plus the safety work that comes with moving machines.
How is IoRT used in logistics?
IoRT is used in logistics mainly inside warehouses and distribution centers, where connected robots move inventory, pick orders, sort parcels and report every step to the management software. Most Internet of Robotic Things logistics projects start in the warehouse, and the common applications fall into a few groups.
Autonomous mobile robots
The autonomous mobile robots logistics operators deploy most often navigate using onboard sensors and maps rather than fixed tracks. Some carry totes or carts alongside human pickers, guiding them to the next location. Others move shelves, pallets or bins across the floor. Because each robot reports its position and task status, the system can rebalance work between zones, send robots to charge at quiet times and show supervisors where every order stands.
Goods-to-person systems
In a goods-to-person design, robots bring the storage location to a fixed workstation instead of sending a person to walk the aisles. Amazon's March 2012 agreement to acquire Kiva Systems, a material handling technology company, is often cited as the moment this model became a mainstream warehouse strategy. Since then, several vendors have offered mobile shelf-moving robots, grid-based storage systems with robots on top and shuttle systems inside racking. The approaches differ in density, throughput and how easily they can be changed later, so the right choice depends on the order profile rather than the brand.
Robotic picking and packing
Picking individual items of different shapes from a bin remains one of the harder problems in warehouse robotics. Robotic arms with machine vision and grippers can now handle many standard items, and the IoRT link lets them learn from failed picks, hand difficult items to people and report throughput. For many sites, robotic picking works best for consistent packaging and a stable product range, while people still handle exceptions, damaged items and unusual sizes.
Inventory monitoring and predictive maintenance
Connected robots and sensors also improve inventory visibility. The Sensors review lists smart shelving that flags replenishment needs and sensors that predict when logistics robots need service. In practice, cycle counts, location audits and maintenance alerts become routine data rather than manual projects.
Beyond the warehouse
Outside the building, IoRT ideas extend to autonomous trucks, delivery robots and drones that report location and condition data during transport. These are earlier in adoption and face their own regulatory questions. Our article on the road to autonomous trucking covers the transport side, and our overview of AI and real-time monitoring in cold chain logistics explains how connected sensors already protect temperature-sensitive shipments in transit.
What are the challenges of the Internet of Robotic Things?
The main challenges of the Internet of Robotic Things are integrating systems from different vendors, securing a larger connected network, keeping communication fast and reliable, justifying the cost and handling the exceptions that robots cannot resolve on their own. Each one can stall a project that looked simple in a demonstration.
Integration and interoperability
Many warehouses run several automation systems that each work well alone but were not designed to work together. A mobile robot fleet, an automated storage system, a conveyor and the warehouse management system may each have their own software and data formats. Early adopters often find that the hardest work is making these systems share tasks and status in real time. Middleware and integration platforms help by translating between systems, and some sites use a digital twin, a virtual model of the warehouse, to test layout changes and what-if scenarios before changing the real floor. Neither removes the need for clear ownership of the end-to-end process.
Cybersecurity
Every connected robot, sensor and controller is a possible entry point. The Sensors review identifies vulnerability to cyberattacks, unauthorized access and data manipulation as a central IoRT challenge and points to encryption, authentication and regular updates as basic protections. In a warehouse, a breach can do more than leak data. It can stop robots, change task assignments or corrupt inventory records. Robot networks should therefore be segmented from office systems, vendor remote access should be controlled and logged, and software updates should follow a tested change process.
Latency and network reliability
Robots that coordinate in real time need low latency and stable wireless coverage across the whole building, including cold rooms, mezzanines and areas with dense metal racking. Edge computing reduces the distance data has to travel, but the site still needs a plan for what robots do if the network drops, and how work continues manually until it returns.
Cost and return on investment
Robotic systems require capital or subscription spending, integration work, facility changes and training. The return depends on order volume, labor availability, accuracy requirements and how long the layout will stay stable. A site with low volume or frequent product changes may get better results from scanning, slotting and process improvements before adding robots.
Safety and exceptions
Robots that share space with people raise safety questions that fixed machines do not. OSHA's robotics safety page notes that many robot accidents occur during non-routine conditions such as programming, maintenance, testing and adjustment, and that there are currently no OSHA standards written specifically for the robotics industry, so employers rely on general duty obligations and consensus standards. Exceptions also remain a practical limit. Damaged cartons, mislabeled items, blocked aisles and unusual orders still need people with clear procedures to resolve them.
How can healthcare warehouses use robotics?
Healthcare warehouses can use robotics to improve picking accuracy, inventory traceability and throughput for medical devices, diagnostic kits, laboratory supplies and pharmaceuticals, as long as the automated process still meets the quality, temperature and record-keeping rules that apply to those products. Robotics in healthcare warehousing is less about speed alone and more about reducing errors that are costly to correct.
For drug products, US current good manufacturing practice rules in 21 CFR part 211, subpart H require written warehousing procedures that include quarantine before release by the quality control unit and storage under appropriate temperature, humidity and light conditions. The same subpart requires distribution procedures in which the oldest approved stock is generally distributed first and the distribution of each lot can be readily determined in case of a recall. Connected robots and software can support these controls well, for example by blocking picks from quarantined locations, enforcing first-expired or oldest-stock rules and logging lot numbers at every move. They can also fail them if the configuration is wrong, so the automated logic needs to be validated like any other quality-relevant system.
A practical way to decide where robotics fits is to work through these steps:
Map the products and their rules. List temperature ranges, controlled or hazardous items, lot and expiry tracking needs and any serialization requirements before choosing equipment.
Measure the current process. Record order lines per day, pick error rates, travel time and peak patterns so the business case is based on real data.
Start with the highest-error or highest-volume flow. Picking of small, repetitive items or replenishment of kitting stations often gives the clearest gains.
Check the environment. Confirm that robots, batteries and wireless coverage are rated for cold rooms or freezers if they will operate there.
Validate and document. Test how the system handles quarantine, expiry, recalls and network loss, and write the results into your standard operating procedures.
Plan the human role. Define who handles exceptions, who can override the system and how staff are trained to work safely around moving robots.
Temperature-controlled storage adds its own conditions. Robots that work in refrigerated or frozen areas face condensation, battery performance limits and door management between zones. Some sites keep robotics in ambient picking areas and use connected sensors and monitoring in cold rooms instead. Our guide to GMP warehousing and our GMP biologics storage warehouse checklist explain the quality framework any automated system has to fit into.
Where IoRT is heading in logistics
Most warehouses today run a mix of manual processes, standalone machines and a few connected robot fleets. The direction of travel is toward systems that share one view of inventory and tasks, with robots, sensors and people coordinated by software rather than by separate control rooms. Progress depends less on single breakthroughs and more on steady work in integration standards, security practice, picking technology and staff training.
For life science and healthcare shippers, the practical question is not whether to automate everything, but which steps carry the most risk of error or delay and whether connected robotics, better sensors or a better process is the right answer for each. Emerging tools are covered in our article on emerging technologies in cold chain logistics.
Mercury's warehousing and fulfillment and cold chain storage and warehousing services support life science clients with temperature-controlled storage, inventory management systems and daily reporting on shipped materials, which gives teams the visibility they need whether their own operations are manual or automated.
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