Spatio-Temporal Representations and Activities For Cognitive Control in Long-Term Scenarios (STRANDS)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive Systems and Robotics
Action type: Collaborative project
Countries involved: United Kingdom,Sweden,Germany,Austria,United Kingdom,United Kingdom,United Kingdom
Call:  (FP7-ICT-2011-9, 2011)
Start date: 2013-04-01
End date: 2017-05-31
Duration (months): 50.0
Total project cost (EUR): 10774552.0
EU funding (EUR): 8234543.0
Participants: 8
Project status: CLOSED

STRANDS aims to enable a robot to achieve robust and intelligent behaviour in human environments through adaptation to, and the exploitation of, long-term experience. Our approach is based on understanding 3D space and how it changes over time, from milliseconds to months. We will develop novel approaches to extract spatio-temporal structure from sensor data gathered during months of autonomous operation. Extracted structure will include reoccurring 3D shapes, objects, people, and models of activity. We will also develop control mechanisms which exploit these structures to yield adaptive behaviour in highly demanding, real-world security and care scenarios. The spatio-temporal dynamics presented by such scenarios (e.g. humans moving, furniture changing position, objects (re-)appearing) are largely treated as anomalous readings by state-of-the-art robots. Errors introduced by these readings accumulate over the lifetime of such systems, preventing many of them from running for more than a few hours. By autonomously modelling spatio-temporal dynamics, our robots will be able run for significantly longer than current systems (at least 120 days by the end of the project). Long runtimes provide previously unattainable opportunities for a robot to learn about its world. Our systems will take these opportunities, advancing long-term mapping, life-long learning about objects, person tracking, human activity recognition and self-motivated behaviour generation. The extraction of structure is key to this, as it both captures potential meaning, and also compresses a robot's sensor data into representations capable of storing months of experience in a manageable form. We will integrate our advances into complete cognitive systems to be deployed and evaluated at two end-user sites: a care home for the elderly in Austria, and an office environment patrolled by a security firm in the UK. The tasks these systems will perform are impossible without long-term adaptation to spatio-temporal dynamics, yet they are tasks demanded by early adopters of cognitive robots. We will measure our progress by benchmarking these systems against detailed user requirements and a range of objective criteria including measures of system runtime and autonomous behaviour. STRANDS will produce a wide variety for results, from software components to an evaluation of robot assistants for care staff. These results will benefit society in a range of ways: researchers will be able to access our results as open-access papers, software and data; our methodology for creating long-running robots will encourage roboticists to tackle this unsolved problem in our field; industrialists will see how cognitive robots can play a key role in their businesses, and access prototypes for their own use; and society will benefit as robots become more capable of assisting humans, a necessary advance due to, for example, the demographic shifts in the health industry.

Keywords: robotics,long-term autonomy,mapping,object recognition,people detection and tracking,qualitative spatial representations,activity recognition,planning,user acceptance,security,care