Robotics and Machine Perception laboratory

The RuM laboratory develops technologies that enable computerized systems to perceive the world, interpret it, make decisions, and act. We believe that robotics, artificial perception, and artificial intelligence will play an increasingly important role in human development, both in everyday life, industry and economy, as well as politics. EDI RuM's goal is to become a significant player in shaping this future. A player whose research results and developed technologies are not only valued in their scientific field but also promote human welfare, safety, and health. So far, we have developed and adapted our laboratory technologies for various applications in the fields of medicine, industry, agriculture, and mobility.

Keywords:

• machine learning, deep neural networks, synthetic training data
• artificial intelligence - explainable, generative, physics-informed
• automation, industrial robots, real-time control, mobile robots
• embedded intelligence and computer vision
• program synthesis

The laboratory consists of four research groups - three of which focus on different aspects of artificial intelligence and one specializes in robotics research.

The robotics group led by Ph.D. Jānis Ārents has long-term goals aimed at enabling robots to operate effectively in complex, unstructured environments and interact seamlessly with humans. To achieve this, our work is focused on developing fundamental autonomous mobile navigation capabilities in unstructured environments, as seen in projects like VIZTA, EdgeAI, and MOTE, which explore the use of computer vision and other sensor modalities for perception and semantic scene interpretation. The main goal is to equip robots with the ability to perform general-purpose manipulation and interaction, as illustrated by our work in industrial manipulation within TRINITY and IMOCO4.E, as well as our efforts to facilitate robot-human interaction through projects like AI4DI. This involves not only enhanced perception but also the development of high-level reasoning and task execution, where projects like EdgeAI focus on parsing natural language instructions and grounding them in environmental context using compact, interpretable AI models. Furthermore, we are committed to integrating advanced perception and sensors, including unconventional modalities, with projects like MOTE and EdgeAI adapting open-ended semantic perception to real-world conditions and real-time constraints, allowing for rapid integration of new observations and task-specific information extraction, such as traversable ground segmentation. Our work in recent years, including the development of semantic scene interpretation (MOTE), natural language grounding (EdgeAI), and human interaction (AI4DI), has directly contributed to these goals, enabling robots to understand and operate in open, dynamic environments. We have also focused on developing perception systems that can operate effectively under real-world conditions and real-time constraints, as demonstrated in MOTE and EdgeAI, and creating planning systems that can understand natural language instructions and ground them in the environment using efficient AI models, which is a key aspect of EdgeAI. With these ongoing efforts, we are shaping a future where robots can operate autonomously and collaboratively across a wide range of complex real-world scenarios.

Dr. sc. comp. Kaspars Sudars' group aims to develop better artificial intelligence that leads to scientific excellence, as well as develop AI-based technology that leads to commercialization opportunities. The main theme of the group is explainable artificial intelligence and its methods. In this direction, the group researches fundamental AI models for intelligent system development and uses explainable artificial intelligence to identify relevant data features responsible for AI decision-making. Explainable AI is particularly important in healthcare applications, for which the group has conducted national grant projects OSTAK and LU-AIDA. The group also trains deep neural network models in various industry applications with a high technology readiness level (TRL).

The general scientific direction of the group led by Dr. sc. comp. Kārlis Freivalds is machine learning and artificial intelligence. An additional direction is the creation and application of various mathematical algorithms and the creation of innovative devices based on mathematical algorithms. The most important application currently is in robotics, for example within the international project AIMS5.0. Additionally, the group works with fundamental artificial intelligence topics - physics-informed AI and program synthesis.

The laboratory head Dr. sc. ing. Roberts Kadiķis' group works on solving computer vision tasks (object detection, image classification, and segmentation) using both machine learning methods and classical image processing approaches. A significant research area is generative AI (generative adversarial networks, diffusion models), which we use for generating synthetic training data. For example, within the ERDF project AimOOC, generative AI was used to synthesize images of organs cultivated on chips, which were then used to train image classifiers for automating the cultivation process. The group actively seeks new applications for generative AI, resulting in solutions such as virtual medical image staining (LZP project HAVeT-AI) and making 3D modelled synhtetic data look more like real images. Models whose perception capabilities have been enhanced with various data synthesis and data augmentation methods are further applied in mobile systems (projects COMP4DRONES, Augmented CCAM), industrial robot systems (VIZTA IMOCO4.E), and for medical image analysis.