Embodied AI and Robotic Vision Research Group

Research group part of the Australian Institute for Machine Learning

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Embodied AI and Robotic Vision Research Group

We develop embodied AI systems that perceive, reason, navigate, and act reliably in complex open-world environments.

The Embodied AI and Robotic Vision Research Group develops perception, learning, and reasoning capabilities for autonomous robots. The group sits within the Australian Institute for Machine Learning (AIML), Adelaide University, and connects robotics, computer vision, machine learning, and embodied AI.

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Research Themes

Reliable Deep Learning for Robotics

We build perception systems that remain trustworthy beyond closed-set accuracy benchmarks. This includes out-of-distribution and unknown-class detection, open-world object detection, uncertainty estimation for identifying open-set errors, and run-time monitoring of model performance during deployment. The goal is robotic systems that recognise novel objects, flag when inputs violate their training assumptions, and fail safely in unstructured real-world conditions.

Embodied AI and Robotic Vision

We study perception that is active and grounded in an agent acting in the world rather than passive image analysis. This includes embodied object detection with spatial feature memory, embodied domain adaptation for detectors deployed on robots, physically grounded 3D world models for prediction and control, robot manipulation (generalist manipulation policies, data-driven dexterous manipulation), and multimodal perception under degraded or missing sensing.

Open-World Navigation, Mapping, and Localisation

We develop representations and methods that let robots navigate environments they were not explicitly mapped for. This includes topological maps for open-world visual navigation, traversability-aware navigation to topological goals, fine-grained cross-view geo-localisation between ground and satellite imagery, scene change detection with visual foundation models, and vision-and-language navigation that knows when to ask for missing information.

Adaptation, Generalisation, and Foundation Models for Robotics

We work on closing the gap between training and deployment, and on bringing large pretrained models into embodied settings. This includes source-free and online domain adaptation for object detection, predicting and handling class-distribution shift, foundation models for goal-oriented reinforcement learning and exploration, generalist robot manipulation policies and effective strategies for tuning them, and 3D large multimodal models for scene understanding, moving toward vision–language–action systems.

People

Group Lead

Postdocs / Research Fellows

PhD / MPhil Students

Masters / Honours Students

Alumni

Students

Postdocs / Research Fellows

Funded PhD Opportunity

Self-Aware RGB–Thermal Tracking with Unknown-Object Discovery for Edge-Deployed UAVs

We are seeking an outstanding PhD candidate to join the Embodied AI and Robotic Vision Research Group at the Australian Institute for Machine Learning, Adelaide University.

Autonomous drones and robots must track important targets reliably under changing environmental conditions, recognise when their predictions may be wrong, and detect unexpected objects. This project will develop a compact multimodal vision system combining RGB and thermal sensing for robust, trustworthy perception on edge-deployed UAVs.

Research Focus

The project will investigate:

The research sits at the intersection of computer vision, machine learning, robotics, multimodal perception, and edge AI.

Candidate Background

Applicants should have a strong background in one or more of the following areas:

Strong programming skills are highly desirable.

Scholarship and Eligibility

This project is offered through Adelaide University’s 2026 Signature Research Theme Scholarship Round.

Candidates may submit an Expression of Interest before completing their current degree, subject to the following requirements:

The opportunity is open to domestic and international applicants, subject to the project-specific eligibility requirements detailed in the official listing.

How to Apply

Submit an Expression of Interest directly

To view the official project listing, visit the Adelaide University research-project catalogue and search for SRTSR0161.

Join Us

Prospective PhD Students

Relevant backgrounds include:

Interested applicants should send: