Advanced Adaptive Intelligent Systems
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, with a focus 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 make use of the Intelligence Augmentation Lab that TMH and RPL plan to set up.
Intelligent systems built around big datasets and machine learning techniques are becoming ubiquitous in people’s lives – in the form of 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 continually adapt to the specific task, user constellation, and shared environment they are operating in. 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.
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH), 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 Digitalize in Stockholm 2022 event:
Associate Professor, Department of Robotics, Perception and Learning at KTH, Working group Learn, PI of research project Advanced Adaptive Intelligent Systems (AAIS), Supervisor for Postdoc project Designing Gamified Robot-Enhanced Interventions for Children with Neurodevelopmental Disorders, Supervisor for postdoc project On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People, Digital Futures Facultyiolanda@kth.se
Professor and Dep. Head of Division at Division of Speech, Music and Hearing at KTH, Working group Learn, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Digital Futures fellow, Digital Futures Faculty+46 8 790 89 65
Professor and Head of Division, Division of Speech, Music and Hearing at KTH, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Digital Futures Faculty+46 8 790 89 65
Assistant Professor, Department of Computer and Systems Sciences at Stockholm University, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), Supervisor for postdoc project On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People, Digital Futures Faculty08-16 16 81
Assistant Professor, Division of Technology in Health Care at KTH, Co-PI of research project Advanced Adaptive Intelligent Systems (AAIS), 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