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, 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.

Background
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.

Crossdisciplinary collaboration
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:

Articles:

Avancerade adaptiva intelligenta system