NEST Environments

WASP supports nine multidisciplinary world-leading research environments and networks within AI, Autonomous Systems, and Software, characterized by Novelty, Excellence, Synergy, and Team, NEST.

The NEST initiative is a major investment in multidisciplinary research environments and networks. The underpinning idea is to address hard, challenging research questions that require multidisciplinary efforts. Thus, they have strong Principal Investigators to join forces and form teams that are equipped to address these problems, which have potentially very high academic, industrial and societal impact.

In response to the first call for NESTs in early 2021, 35 applications were submitted involving more than 100 individual researchers. After undergoing international peer-review, nine proposals were awarded grants for five years. Each NEST involves four to six established researchers, including several WASP recruited faculty members. All WASP partner universities are represented in the NESTs, as well as several companies. The purpose of these nine new research environments is to strengthen Sweden’s competitiveness.

The NESTs

DISCOWER: Distributed Control in Weightless Environments

Space and subsea environments are two of the most challenging among the emerging fields of autonomous systems. This project aims at filling the gap in the state-of-the-art on trans-environmental multi-robot control and planning.

NEST-Project DISCOWER

_main_: Multi-dimensional Alignment and Integration of Physical and Virtual Worlds

Seamlessly integrating simulation models with machine learning can take AI systems to a whole new level. _main_ aims to establish a new modeling paradigm for end-to-end learning and systematic integration of data and simulators.

NEST-project __main__

STING - Synthesis and Analysis with Transducers and Invertible Neural Generators

In this project we develop a unified machine-learning framework that accounts for the entire range of communication modalities, and both for analysing and generating human communication. For better human-robot communication.

NEST-project STING

PerCorSo: Perceiving and Communicating Correct-by-design Socially Acceptable Autonomous Systems

PerCorSo aims to design autonomous behaviors of interacting robots that are not only guaranteed to be safe but are also perceived as safe and accepted by people.

NEST-project PerCorSo

CyberSecIT: Automated and Autonomous Cybersecurity for IoT

The CyberSecIT-project addresses two core challenges at the heart of IoT security: Automation, enabled by software and, Autonomy, enabled by machine learning technologies.

NEST-project CyberSecIT

Learning in Networks: Structure, Dynamics, and Control

Understanding the behavior of large network systems and how to configure them through automated and decentralized management.

Data-bound Computing

Data bound computing aims at developing the principles behind the next generation of computing systems, optimized for modern data-intensive workloads.

Intelligent Cloud Robotics for Real-Time Manipulation at Scale

Investigating the areas of fault tolerance, edge-cloud control and resource allocation, as well as federated, continual and transfer learning, learning at scale in the context of a cloud robotics demonstrator system.

3D Scene Perception, Embeddings and Neural Rendering

Developing new theory and algorithms to make it possible to manipulate, modify, and auto-complete a semantically and geometrically meaningful 3D representation of the world.

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