About the project
Objective
The objective of this project is to learn distributed control policies for multi-robot systems that scale on-demand, in multi-laterally evolving complex dynamic environments.
Background
Multi-robot systems such as drone swarms offer unparalleled advantages in assisting ground-based human-robot teams in humanitarian, and disaster-response missions thanks to their ability to operate in remote, communication-denied environments. However, in practical deployments such systems must learn how to balance various auxiliary objectives on-demand for maximizing the collective utility in highly dynamic environments. For example, a robust drone swarm control policy must prioritize the most severely affected area in a disaster-response event, and direct more robots to tackle it, on-the-fly.
In this project, we aim to design a framework that can help us learn such control policies while taking into account the robot’s varying capabilities. Upon designing such a framework, we further understand that we can minimize the human bias in hand-engineered task prioritizing.
About the Digital Futures Postdoc Fellow
Malintha Fernando received his Ph.D in 2023 from Indiana University, Bloomington (USA). His doctoral research focused on designing cooperative scalable control policies for multi-drone systems that are robust to communication failures. Such policies lend themselves to many Urban Air Mobility (UAM) applications such as autonomous parcel delivery, where decentralized decision-making under local information is required to achieve the necessary scalability over the geographical span and in the number of vehicles.
Prior to joining KTH, Malintha worked as a visiting lecturer in Machine Learning at Indiana University. He has completed an internship at Open Robotics, Mountain View, and completed his undergraduate education from University of Moratuwa, Sri Lanka.
Main supervisor
Silun Zhang, Assistant Professor, Division of Optimization and System Theory, Department of Mathematics, KTH.
Co-supervisor
Petter Ögren, Professor, Division of Robotics, Perception and Learning (RPL) at KTH.
About the project
Objective
This research aims to develop data-driven models for gait biomechanics to improve precision rehabilitation. By integrating statistical shape modeling, musculoskeletal simulations, and deep reinforcement learning, the project enables personalized gait impairment assessments and optimizes rehabilitation interventions for individuals with neurological and musculoskeletal disorders.
Background
Human gait is a complex biomechanical process influenced by neuromuscular control, skeletal structure, and external factors. Understanding gait abnormalities is essential for designing effective rehabilitation strategies. Traditional gait analysis, relying on motion capture and inverse dynamics, has limitations in scalability, personalization, and real-time applicability.
Recent advancements in artificial intelligence, musculoskeletal modeling, and wearable technology offer new opportunities for precision rehabilitation. Statistical shape modeling enables personalized bone and muscle geometry reconstruction, while deep reinforcement learning facilitates adaptive gait retraining strategies. This research integrates these approaches to develop predictive models that bridge the gap between clinical gait analysis and real-world rehabilitation applications.
About the Digital Futures Postdoc Fellow
Liangliang Xiang is a researcher in biomechanics and computational modeling. He holds a PhD in Bioengineering from the Auckland Bioengineering Institute, University of Auckland. His research focuses on gait biomechanics, musculoskeletal modeling, and explainable AI for movement analysis. He has developed predictive models for bone stress in running, integrated wearable sensors into biomechanical simulations, and applied deep learning for human movement analysis. He focuses on translating computational biomechanics into practical applications for gait rehabilitation.
Main supervisor
Elena Gutierrez Farewik, Professor, Department of Engineering Mechanics, KTH.
Co-supervisor
Ruoli Wang, Assistant Professor, Department of Engineering Mechanics, KTH.
About the project
Objective
The aim of this project is to explore and assess the potential of novel shape-changing wearables to improve body-based communication. These technologies hold promise because they can be worn on the body and provide tangible, haptic actuation that can emulate qualities of collocated physical interaction, as well as open up novel interactive qualities altogether.
A key concept that the project addresses is that of connecting bodies. I will explore how the interactive qualities of shape-changing wearables can be designed and used to foster a somatic connection between bodies, e.g., bridging together actions, perceptions and emotions from one body to another in a way that they are felt by the person, rather than just narrated. I envision that fostering this felt connection can, in turn, create richer, more effective and affective body-based communication.
Background
The digitalization of society, as well as recent global health developments (i.e. the COVID-19 pandemic), have fostered a shift from face-to-face, collocated interactions to remote communications. Communicating over video-mediated online platforms and conferencing software is becoming pervasive, shaping our everyday lives, practices, and how we interact with each other. Yet, these solutions do not adequately support settings where body-based interaction and physical contact are critical for effective, affective and rich communication, for example, remote health practices (e.g. remote physiotherapy) or affective well-being settings (e.g. long-distance relationships).
About the Digital Futures Postdoc Fellow
Laia Turmo Vidal is an interaction design researcher. Her research focuses on the design, development and evaluation of multi-sensory technologies that enrich the aesthetic perception of the body as a way to promote rich physical, emotional, and social experiences. Her research targets domains of health and wellbeing such as sports, fitness, rehabilitation and dance. Her research interests include wearable technology, material explorations, social cooperation and design methods development.
Laia holds a PhD and an MSc in Human-Computer Interaction from Uppsala University (Sweden) and a BDes in Multimedia Technologies from Universitat Politècnica de Catalunya (Spain). Prior to KTH, she was a postdoctoral researcher at i_mBODY Lab at the University Carlos III de Madrid (Spain). She has also been a research intern at UCL Interaction Center (UK) and a research visitor at the University of California, Santa Cruz (USA).
Main supervisor
Kristina Höök, Professor, Division of Media Technology and Interaction Design, KTH
Co-supervisor
Georgios Andrikopoulos, Assistant Professor at the School of Industrial Engineering and Management, KTH
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to engineer the rules by which intelligent robots interact with each other in public spaces. In particular, this project focuses on tools from the field of mechanism design, which strives to set rules for interaction between rational agents. Designing rules via mechanism design allows for systems where robots collaborate toward global goals, even when their individual goals and specifications differ. Mechanism design, coupled with tools from formal methods and planning, can help achieve global goals like safely sharing resources, minimizing the risk of failures in uncertain systems, and engendering the trust of humans.
Background
Multi-robot planning is a complex optimization problem that must consider both the goals of each robot and the interaction between those goals. The first step toward solving this problem requires understanding the realities of modern-day robots. The second step requires meta-reasoning through game theory. This problem becomes more complicated with the introduction of humans, who interact with robots in unique and often unpredictable ways.
About the Digital Futures Postdoc Fellow
Anna Gautier is a postdoctoral researcher in artificial intelligence and robotics. She works in the Robotics, Perception and Learning division at KTH Royal Institute of Technology. Anna obtained her PhD from the University of Oxford in July 2023, with a thesis entitled “Resource Allocation for Constrained Multi-Agent Systems.” Her research interests include multi-agent systems, human-robot interaction, planning under uncertainty, formal methods and mechanism design.
Main supervisor
Jana Tumova, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Co-supervisor
Iolanda Leite, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Watch the recorded presentation at the Digitalize in Stockholm 2023 event
About the project
Objective
The overall objective is to develop and evaluate AI-based and classical optimization (mathematical programming) approaches for sensor placement and control, data processing, communication, and motion planning.
This project aims to develop novel algorithms and computational methods for optimal planning, deployment, and operations of a network of AUVs and sensors in undersea environments. These algorithms will focus on optimizing the placement of sensors and movement of AUVs across the region, paying attention to relevant objectives including coverage and communication robustness. They will also focus on processing locally collected AUV measurements (e.g., sonar data) for situational awareness tasks (e.g., the emergence of an adversarial threat). The project will consider classical model-based approaches, AI/ML approaches, and hybrid approaches to developing these algorithms, comparing and contrasting them under different environmental settings and dynamics.
Of particular interest in this project is coordinating sensors and fleets of autonomous underwater vehicles (AUVs) to patrol regions of the ocean. However, these settings pose unique challenges in placement/motion planning such as limited communication, computations, and data processing. The communication challenges are mainly dealt with in a second sub-project led by the researchers at Purdue University.
Background
Surveillance systems are becoming increasingly reliant on the ability of autonomous networked agents to conduct intelligence, surveillance, and reconnaissance (ISR) tasks. Much recent effort has been devoted to AI/ML-based approaches for augmenting such systems, though pinpointing exactly when AI/ML gives clear-cut advantages over traditional optimization and analytics-based is still an open question. Moreover, while many existing efforts in autonomy are focused on systems of drones, sensors, and other vehicles that operate above the surface, little attention has been paid to undersea ISR settings. The project focuses on the undersea setting, bringing together expertise from different fields, and forms a new line of collaboration between KTH, Purdue University, and Saab.
Crossdisciplinary collaboration
This project is part of a larger collaboration between Saab, KTH, and Purdue University. The project focuses on developing novel algorithms and computational methods for, planning, deploying, controlling, and operating a network of AUVs and different types of sensors over contested undersea environments.
The project brings together expertise from Applied Mathematics, Optimization, Electrical Engineering, and expertise in Underwater Environments.
Participating in the project:
- PhD student at KTH (recruitment ongoing)
- Jan Kronqvist, KTH, PI
- Roger Berg, Saab, PI
- Per Enqvist, KTH, co-PI
Collaborators in the larger project:
- Christopher Brinton, PI, Purdue University
- Shreyas Sundaram, co-PI, Purdue University
About the project
Objective
The ALARS project aims to drastically improve how Unmanned Underwater Vehicles (UUVs) are deployed and recovered in maritime operations. Currently, UUV launch and retrieval rely on manual, time-consuming, and high-risk methodsinvolving surface vessels and human intervention. ALARS introduces an autonomous aerial solution that integrates drone (UAV) technology with UUV operations, significantly enhancing efficiency, safety, and scalability in underwater missions.
Key features of ALARS:
- UAV-Based UUV Deployment – Autonomous drones will carry and release UUVs precisely at mission locations.
- Automated Recovery System – UAVs equipped with an advanced winch mechanism will retrieve UUVs from the water without requiring human intervention.
- AI-Powered Navigation & Stability Control – The system leverages machine learning algorithms for object detection and dynamic stability during deployment and retrieval.
- Multi-UUV Operations – ALARS supports the simultaneous launch and recovery of multiple UUVs, significantly boosting operational capacity.
By automating these processes, ALARS aims to reduce human risk, increase mission success rates, and unlock new capabilities in maritime security, environmental monitoring, and offshore industries.
Background
Modern UUV operations are essential for naval intelligence, surveillance, reconnaissance (ISR), environmental monitoring, and subsea exploration. However, traditional deployment and recovery methods rely on mothership-based handling, which presents multiple challenges:
- Operational inefficiency – Manual operations take time and limit real-time responses.
- Safety concerns – Harsh weather and ocean conditions pose risks to human operators.
- Limited scalability – Only one UUV can be deployed or retrieved at a time.
ALARS directly addresses these limitations by integrating aerial robotics with autonomous underwater systems, allowing for:
- Faster and more flexible UUV deployment and recovery.
- Reduced risk to human operators by eliminating manual handling.
- Multi-domain autonomy with real-time AI-powered decision-making.
By leveraging Sweden’s expertise in robotics, AI, and autonomous systems, ALARS sets a new global benchmark for efficient and safe maritime operations.
Crossdisciplinary collaboration
The ALARS project brings together experts in:
- Autonomous Underwater Systems – UUV development and deployment strategies (KTH, SMaRC)
- Artificial Intelligence & Machine Learning – AI-driven target detection and real-time decision-making (KTH)
- Maritime Defense & ISR Operations – Application-specific design for naval and offshore use cases (Saab Kockums)
The project is a collaboration between KTH, Saab Kockums, and SMaRC, with direct industry involvement to ensure real-world validation and future deployment.
Principal Investigators (PIs)
- Ivan Stenius (KTH, Project Lead)
- John Folkesson (KTH, AI & Machine Learning Integration)
- Petter Ögren (KTH, Multi-Agent Coordination and Control Systems)
About the project
Objective
The Digital Futures Drone Gymnasium explores the potential of physical and embodied training accessories to support drone programming and their interactions with humans. The project sits at the intersection of mobile robotics, autonomous systems, machine learning, and human-computer interaction, providing tools to study and envision novel relationships between humans and robots.
Training accessories are tools that allow us to better understand how drones can be effectively operated in work and living spaces. Our training accessories physicalise the control mappings of our machines, which results in the distribution of the cognitive load of controlling a drone over the whole body.
Background
The project follows in the footsteps of the earlier DF Demonstrator Project Drone Arena and largely involves the same research team. The PIs are at the forefront of their respective research fields and provide a unique and complementary combination of expertise. The multiple awards and recognitions obtained by Prof. Luca Mottola in the field of aerial drones provide a stepping stone for technical work. Prof. Kristina Höök pioneered a design philosophy named Soma Design of relevance to designing interactions with autonomous or semi-autonomous systems, such as drones.
The drone manufacturer Bitcraze, based in Malmö, supports the project and provides a much-needed industry perspective. Dr. Joseph La Delfa, who was previously part of this research team and is now an industrial post doctoral researcher at Bitcraze, will act as a liaison between the Drone Gymnasium and the company. The project is also supported by Rachael Garrett, a PhD candidate at KTH whose research explores ethics in the design of autonomous systems. She also acts as an international collaborator with the Turing AI World-Leading Fellowship Somabotics: Creatively Embodying AI.
Crossdisciplinary collaboration
Prof. Mottola is an expert in mobile robotics and autonomous systems. He focuses on the concrete realisation of the training accessories across hardware and software. Prof. Kristina Höök is a professor in interaction design, specialising towards designing for movement-based interactions between users and autonomous or semi-autonomous systems.
The expertise of the two PIs join in the organisation of the workshops and interactive exhibitions. Successfully accomplishing the project goals, especially related insights from the workshops that might transfer to other application domains will be blending Höök’s skillset with the system expertise of Mottola.