Research @ A2I

The research we do at A2I spans a diverse range of application areas, from robot perception to reinforcement learning and learning from demonstration. Our focus lies on exploring fundamental challenges at the intersection of machine learning, AI, and robotics.

Here’s a closer look at some of the exciting work going on in the group…



2206, 2017

Adversarial Domain Adaptation

Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While [...]

704, 2017

Deep Tracking

Deep tracking in the wild: End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks Abstract – This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban [...]

903, 2017

Auditory perception

Leveraging the Urban Soundscape Abstract - Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be [...]

2909, 2014


In the context of decision making in robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework [...]