AI Robot for Environmental Recognition Tested at HSE University
Engineers and researchers at the HSE Institute for Robotics Systems have successfully run the first tests of an AI-powered environmental recognition model deployed on a robot dog. The robot navigated around the HSE Pokrovka building and Pokrovsky Bulvar, analysing the surrounding environment to identify its location.
Work on the neural network model and software has been underway since April in partnership with EFKO. The creators call this system a 'scene classifier.' It captures images from the robot's camera to identify the type of environment it is in. The classifier recognises more than 60 distinct categories, such as rooms, streets, courtyards, and parks.
Sergey Slastnikov
‘We developed and trained a neural network model capable of analysing images from the robot's camera to classify them into over 50 environment types. Prior to this, the model was only run on a standard computer. This current test was essential to transfer the software directly to the robot's onboard hardware and verify its operation in real-world environments,’ noted Sergey Slastnikov, Deputy Director of the HSE Institute for Robotics Systems.
A primary goal of the testing was to evaluate how effectively the model functions directly on the robot. The team guided the robot through buildings and streets, gathering data and logging the specific environmental characteristics the system could identify. Following the tests, the team computed the model's evaluation metrics.
Across all classification tasks—including space type, weather, season, and time of day—the final Micro F1 score reached 93.6%. This summary metric aggregates results across all classes to show overall performance, making it ideal for datasets with varying category sizes. These results demonstrate that the model delivers high-quality environmental recognition under real-world conditions.
Additionally, the team assessed computational performance, focusing on the model's processing speed on the onboard computer. These metrics are vital for the future integration of the classifier into the robot's adaptive control system.
The successful trials represent a major development milestone, serving as the first validation of running the model directly on the robot's hardware.
‘This result is crucial for our current robotics projects—anthropomorphic delivery droids and robot dogs designed for city logistics. For such platforms to operate safely and efficiently in an urban environment, their software must provide autonomous positioning: the robot needs to understand where it is and what is happening around it, and adapt its behaviour accordingly. Testing the scene classifier directly on the robot's onboard hardware is a major step toward the real-world deployment of autonomous couriers in urban conditions,’ noted Andrey Blokhin, IT Innovation Director at EFKO Group.
‘This model isn't the final product; it's an intermediate step toward a more sophisticated adaptive control system. Moving forward, the robot will use scene data to dynamically adjust its behaviour depending on its surroundings. For example, when navigating winter streets, the robot will be able to account for the presence of snow and adjust its movement parameters, or adopt a more cautious driving style when encountering a dense crowd,’ noted Sergey Slastnikov.
Thus, the project's objective goes beyond simple image recognition. Scene detection must serve as the foundation for the adaptive control of the robot's behaviour—specifically its speed, trajectory, and motion parameters—and, looking forward, the behaviour of the entire system.
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