Publications

Daniel Angelov, PhD (2020). From Demonstration to Task-Space Specification: Using causal analysis to extract rule parameterization from demonstrations. In Autonomous Agents and Multi-Agent Systems 2020 Vol(34):2.

Daniel Angelov, PhD, Yordan Hristov, Michael Burke, Subramanian Ramamoorthy (2020). Composing Diverse Policies for Temporally Extended Tasks. In IEEE RA-L.

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Michael Burke, Katie Lu, Daniel Angelov, PhD, Artūras Straižys, Craig Innes, Kartic Subr, Subramanian Ramamoorthy (2020). Learning robotic ultrasound scanning using probabilistic temporal ranking.

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Yordan Hristov, Daniel Angelov, PhD, Michael Burke, Alex Lascarides, Subramanian Ramamoorthy (2019). Disentangled Relational Representations for Explaining and Learning from Demonstration. In CoRL19.

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Daniel Angelov, PhD, Yordan Hristov, Subramanian Ramamoorthy (2019). Using Causal Analysis to Learn Specifications from Task Demonstrations. In AAMAS19.

Daniel Angelov, PhD, Yordan Hristov, Subramanian Ramamoorthy (2018). DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL. In RLDM19.

Daniel Angelov, PhD, Subramanian Ramamoorthy (2018). Learning from demonstration of trajectory preferences through causal modeling and inference. RSS CIR.