Testing of Learning Robots (T-Largo)

Testing of Learning Robots (T-Largo)

01.09.2018 - 01.03.2022


The future of industrial robotics is rooted in the development of robots that can collaborate and learn with humans. These collaborative robots would have the ability to evolve and improve their behaviours through the usage of machine learning algorithms. However, understanding how to control and test the learning skills of uncaged, single- or multi-arm robots and their ability to safely interact with humans is challenging as their expected improvements is not precisely known. Testing such robots is becoming a crucial research area where the combination of expertise in software testing, machine learning and robotics is strongly required. The ambition of the multi-disciplinary T-LARGO project is to develop a new scientific and technological foundation enabling the testing of learning collaborative robots. Its main objective is the construction of an open test platform dedicated to collaborative robots while its impact lies in major scientific breakthroughs on how to test and control robots equipped with artificial intelligence.


Generalizing well-proved software testing methods to learning robots, the T-LARGO project will push forward cutting-edge research in testing and control of advanced industrial collaborative robots. The project will advance the scientific knowledge of how to develop safer collaborative robots with learning capabilities. This is crucial for placing Norway’s robotic ecosystem in the leading position of this research and innovation branch, where some prospective studies say that the market of collaborative robots will increase roughly tenfold between now and 2020.

Aspects relating to the research project

The design of more robust artificial intelligence was recently advocated as one of the most important research directions to follow in the upcoming years. Developing robust artificial intelligence means finding methods that guarantee the safety and dependability of learning processes in various contexts, including robotics. This topic has started to receive considerable attention since the growth of collaborative robotics and machine learning in almost every part of human activities.

However, the rapid development of collaborative robots with learning capabilities is threatened by the lack of open software testing methods able to ensure the safety of these robots. In particular, how to control and test the machine learning algorithms used in robotics is a wide-open scientific question. The T-LARGO project addresses this challenging question through a multi-disciplinary approach combining expertise in software testing, machine learning, and industrial robotics.


The T-LARGO project will start in September 2018 and will last for 42 months. The implementation of the project includes 4 tasks, 4 milestones, and 10 technical deliverables. All the technical deliverables will be publically released.

T1: Machine Learning in Robotics

A comprehensive study of machine learning algorithms used in industrial robotics (reinforcement learning, imitation learning, inductive programming, etc.) is required to understand how the T-LARGO project can advance the State-of-the-Art in the area of testing of industrial collaborative robots. This task, crucial to focus the research on the most important techniques used in robotics, will require to digest an abundant literature extracted from three areas, namely software testing, artificial intelligence, and robotics. In parallel, it is important to examine real-world examples of collaborative robots such as YUMI, UR3 and Tiago, as they are among the most advanced collaborative robots. Understanding the design choices which have guided the development of their learning and optimization abilities is crucial to better test them.

T2: Controlling Constraint Acquisition for Robotic Systems

The theoretical background of constraint acquisition framework is particularly well-understood with many available complexity results. This is clearly an opportunity for developing its adoption in collaborative robotics, where safer actions are requested. Extending the framework for learning new constraints (outside of the bias) and restricted forms of the constraints (safe constraints) will be the key challenges addressed in this task. By controlling constraint acquisition in robotics motion control, this task will enable a fine-grain understanding of the testing challenge of machine learning algorithms.

T3: Testing Machine Learning Algorithms

Machine learning algorithms are notoriously hard to test as their results cannot predicted in advance. Even if the inductive nature of these algorithms makes them almost impossible to test accurately, we believe that combining automatic test generation with partial oracle checking in continuous integration is currently the most promising approach to test these algorithms. The challenge of this task is to propose a general-purpose theoretical framework for testing machine learning algorithms and to support it by the development of an open test platform. Carefully selected checking properties will be examined in the project to serve as partial test oracles and their usage for testing constraint acquisition, reinforcement learning, imitation learning and other learning techniques used in robotics will be explored in depth.

T4: Testing Collaborative Robots Real-World Conditions

A key aspect of T-LARGO is to bring real-world industrial robots into the project (YUMI, UR3, Tiago), so that the research advances will be evaluated and demonstrated on actual robots. This process for evaluating the contributions on real collaborative robots will permit the researchers to revise and update their proposition from the feedback obtained during initial evaluation.

Funding source:

Research Council of Norway (RCN), IKT PLUSS