ANYmal-X robot in an oil platform, photo by Anybotics
Position Information:
- 2 years masters student at VinUniversity.
- Tuition fees fully covered by VinUniversity (100% scholarship).
- Stipends: 20,000,000 VND per month.
- The candidate will work in the research project described below, including design of algorithms, implementation, and conducting experiments.
- Start date: September 2026
- Application deadline:
- First round: April 1, 2026 – May 15, 2026
- Second round: June 1, 2026 – July 15, 2026
How to apply:
- Please follow the instructions for the Masters of Science application at VinUniversity at https://vinuni.edu.vn/graduate-admission/GRE/
- Please mention in your application your interest to work in the project “Dynamic Models for Real-Time Autonomous Decision Making in the Real World”, with Leandro Soriano Marcolino
Project Information:
In recent years, the demand for deploying autonomous robotic systems in complex and high-risk environments, such as natural resource extraction, industrial maintenance, and demining operations, has been rapidly increasing. However, most existing systems still rely heavily on human teleoperation, limiting operational efficiency and increasing costs.
Real-time decision-making plays a critical role in enhancing robotic autonomy. Nevertheless, such systems face multiple sources of uncertainty, including noisy sensors and actuators, incomplete knowledge of the environment, and uncertainties in action outcomes and robot states. Interactions with other systems, including legacy teleoperated robots, further add to the complexity.
This project focuses on developing decision-making approaches under uncertainty, incorporating both stochastic uncertainty from the environment and epistemic uncertainty arising from the simulation models themselves. Instead of relying on a fixed model, the project proposes the use of dynamic models, where the level of detail and accuracy is adaptively adjusted based on the criticality of the state and situation.
This approach enables the use of simpler, faster models in less critical scenarios, while switching to more precise and fine-grained models when evaluating critical situations. As a result, the system can better estimate the expected outcomes of actions, leading to improved decision-making performance in real-world environments.
Research Objectives
The project focuses on the following key objectives:
(i) Develop decision-making methods under uncertainty, integrating both environmental uncertainty and model-based uncertainty.
(ii) Design dynamic models capable of adapting their precision and the granularity of state and action spaces according to the criticality of situations.
(iii) Integrate dynamic models into sampling-based approaches to improve the estimation of expected action outcomes in critical scenarios.
(iv) Investigate interactions between autonomous robotic systems and legacy teleoperated systems in complex real-world environments.
Through these objectives, the project aims to enhance robotic autonomy, improve operational safety and efficiency, reduce costs, and minimize environmental impact in industrial applications.
Applications and Impact
The outcomes of this project have strong potential for real-world applications, including:
- Nuclear decommissioning (in collaboration with UKAEA through partners in Manchester University)
- Maintenance of natural resource extraction facilities (in collaboration with Petrobrás and Vale, through our partners in Vale Institute of Technology and University of São Paulo)
The novel methods and algorithms developed are expected to advance the deployment of autonomous robotic systems in complex real-world environments.
Research Team
VinUni Team:
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External members & Collaborators:
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