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Advanced Adaptive Intelligent Systems

Objective
This project aims to develop adaptive social robots that can understand humans’ communicative behaviour and task-related physical actions and adapt their interaction to suit. We aim to investigate and demonstrate fluid and seamless adaptation of intelligent systems to users’ contexts, needs or preferences. To achieve fluidity, such adaptation needs to happen with minimal interruption to the users’ ongoing interaction with the system, without requiring user intervention, while providing accountability and control of the adaption in a task-appropriate, timely, and understandable manner. This will be explored in multiple embodiments: smart speakers, back-projected robotic heads, and dual-arm robots.

Our use case scenario is an adaptive intelligent kitchen assistant that helps humans prepare food and other kitchen-centric tasks, focusing on supporting ageing in place. Our systems will engage in face-to-face spoken and physical collaboration with humans, track the users’ affective states and task-related actions in real-time, adjust performance based on previous interactions, adapt to user preferences, and show intention using a self-regulation perception-production loop. The project will use the Intelligence Augmentation Lab that TMH and RPL plan to set up.


Background

Intelligent systems built around big datasets and machine learning techniques are becoming ubiquitous in people’s lives – smart appliances, wearables, and, increasingly, robots. As these systems are intended to assist an ever wider range of users in their homes, workplaces or public spaces, a typical one-fits-all approach becomes insufficient. Instead, these systems will need to take advantage of the machine learning techniques upon which they are built to adapt to the specific task, user constellation continually, and shared environment in which they are operating. In long-term deployments, the state of the environment, user preferences, skills, and abilities change and must be adapted. This is relevant for socially assistive robots in people’s homes, education or healthcare settings, and robots working alongside workers in small-scale manufacturing environments.

Crossdisciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH), the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH, KTH) and the Department of Computer and System Science (DSV) at Stockholm University.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Articles:

Avancerade adaptiva intelligenta system

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Contacts

Picture of Iolanda Leite

Iolanda Leite

Associate Professor, Department of Robotics, Perception and Learning at KTH, Working group Learn, PI: Advanced Adaptive Intelligent Systems (AAIS), PI: Adaptive Intelligent Homes (AIH), Former Main supervisor: On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People, Former Main supervisor: Designing Gamified Robot-Enhanced Interventions for Children with Neurodevelopmental Disorders, Digital Futures Faculty

iolanda@kth.se

Jonas Beskow

Professor and Dep. Head of Division at Division of Speech, Music and Hearing at KTH, Working group Learn, Co-PI: Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Digital Futures fellow, Digital Futures Faculty

+46 8 790 89 65
beskow@kth.se

Joakim Gustafson

Professor and Head of Division, Division of Speech, Music and Hearing at KTH, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Digital Futures Faculty

+46 8 790 89 65
jkgu@kth.se

Donald McMillan

Assistant Professor, Department of Computer and Systems Sciences at Stockholm University, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Former Co-Supervisor: On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People, Digital Futures Faculty

08-16 16 81
donald.mcmillan@dsv.su.se
Picture of Sanna Kuoppamäki

Sanna Kuoppamäki

Assistant Professor, Division of Technology in Health Care at KTH, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Co-Supervisor for Postdoc project Personalized Companion Robot for Open-Domain Dialogue in Long-Term Elderly Care, Digital Futures Faculty

+46 8 790 97 31
sannaku@kth.se
Picture of Christian Smith

Christian Smith

Associate Professor, Division of Robotics, Perception and Learning at KTH, Co-PI: Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Digital Futures Faculty

ccs@kth.se