IRAS
The Intelligent and Reliable Autonomous Systems (IRAS) research group focuses on the foundations and applications of sequential decision making, which enables autonomous systems to make complex decisions, while coping with uncertainty, limited information and resources. Our group is particularly interested in ensuring that the single and multi-agent systems are safe, reliable, and unbiased, when operating in fully and partially observable environments. Our methods draw insights from automated planning, reinforcement learning, robotics, decision theory, machine learning, and human factors.
Our current research projects spans four broad themes:
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Building safe and reliable autonomous systems: How can we support the design and deployment of safe and reliable autonomous systems in the open world?
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Sim-to-real transfer: How to develop approaches for efficient sim-to-real policy transfer?
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Human-centered AI: How can we design AI systems that are explainable, equitable, and mindful of human preferences and shortcomings?
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Planning under uncertainty: How can we efficiently express and solve problems involving planning under uncertainty and resource constraints, in single and multi-agent settings?
Recent News
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January 2025: Paper on using credit estimation for mitigating side effects in multi-agent systems accepted to ICRA 2025!
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December 2024: Paper on context-aware multi-objective decision making accepted to AAMAS 2025!
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March 2024: Papers on multi-agent side effects using distributed constraint optimization accepted to FLAIRS 2024 and AAMAS 2024!