Matilda – the Science

Matilda is the culmination of over 20 years of research and over 8 years of development and field trials undertaken during Professor Rajiv Khosla’s Directorship at the Research Centre for Computers, Communication and Social Innovation (RECCSI) at La Trobe University, Melbourne, Australia.

Prof Khosla has long been inspired by the potential of artificial intelligence and machine learning to enhance human qualities. His published research in AI and machine learning with a human-centred approach brought him to NEC’s Innovation Research Laboratories in Nara, Japan. He’s been a central driver in bringing about a social perspective to the design of intelligent robots.

See the timeline of Matilda’s journey here

Designing robots to support human well-being

The design of social robot enabled life-long care systems is all about addressing the social and emotional wellbeing needs of our clients using a range of technologies and smart devices.

Our social robots can perceive human needs using human sensing and tracking attributes such as:

  • language and voice recognition
  • gesture analysis
  • face and emotion tracking 

These features enable the robot to operate with an advanced level of personalisation.

Social robots deliver preventative, proactive and reactive care services by integrating with smart devices such as:

  • tablets
  • smart phones
  • TV
  • health sensors and wearables (e.g., FITBIT, smart watch)

These life-long care systems provide a rich interactional environment with human-like engagement. Our robots can:

  • perceive human needs through their moods and facial expressions
  • connect people to their relatives anytime and anywhere
  • send and receive messages from relatives and carers, and
  • monitor people’s health-related data.

The social robots use the internet/wireless network and cloud computing infrastructure and a range of smart devices to form a unified communication network for our clients and their care providers. Our life-long care eco-system can also collect big data and provide near real-time analytics for monitoring, designing interventions and personalising care.