Robots act on incomplete observations. Computer vision and multimodal perception help a robot recognise objects, interpret scenes and relate its actions to the people around it. Human–robot interaction asks how those capabilities can support understandable and useful behaviour.
This theme connects visual understanding, interactive learning and human-centred autonomy. It includes questions about how a robot can interpret feedback, communicate its intentions and adapt its behaviour to a shared task. The physical setting matters: working near a person places demands on perception, movement and the predictability of the whole system.
Service robotics testbeds provide settings in which perception and interaction must be considered together. Earlier CSE work also proposed using a Baxter robot to investigate how a person’s trust changes as a robot adapts during a shared task, including how much of the adaptation process is made visible to that person.
Explore rUNSWeep, RoboCup Rescue and the work of Francisco Cruz and Yang Song.
The Neuro-Reasoning Lab’s research covers perception, action planning and AI trustworthiness.
Research background
Autonomous adaptation and trust preserves the earlier research proposal.