Craig Innes

According to our database, Craig Innes authored at least 16 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Dialogue-based generation of self-driving simulation scenarios using Large Language Models.

Antonio Valerio Miceli Barone, Alex Lascarides, Craig Innes

CoRR, 2023

Learning rewards from exploratory demonstrations using probabilistic temporal ranking.

Michael Burke, Katie Lu, Daniel Angelov, Arturas Straizys, Craig Innes, Kartic Subr, Subramanian Ramamoorthy

Auton. Robots, 2023

Anticipating Accidents through Reasoned Simulation.

Craig Innes, Andrew Ireland, Yuhui Lin, Subramanian Ramamoorthy

Proceedings of the First International Symposium on Trustworthy Autonomous Systems, 2023

Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models.

Craig Innes, Subramanian Ramamoorthy

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022

Risk-Driven Design of Perception Systems.

Anthony Corso, Sydney M. Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J. Kochenderfer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators.

Manu Lahariya, Craig Innes, Chris Develder, Subramanian Ramamoorthy

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Automated Testing With Temporal Logic Specifications for Robotic Controllers Using Adaptive Experiment Design.

Craig Innes, Subramanian Ramamoorthy

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Robust Learning from Observation with Model Misspecification.

Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Craig Innes, Subramanian Ramamoorthy, Adrian Weller

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications.

Craig Innes, Yordan Hristov, Georgios Kamaras, Subramanian Ramamoorthy

CoRR, 2021

ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments.

Craig Innes, Subramanian Ramamoorthy

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Learning robotic ultrasound scanning using probabilistic temporal ranking.

Michael Burke, Katie Lu, Daniel Angelov, Arturas Straizys, Craig Innes, Kartic Subr, Subramanian Ramamoorthy

CoRR, 2020

Elaborating on Learned Demonstrations with Temporal Logic Specifications.

Craig Innes, Subramanian Ramamoorthy

Proceedings of the Robotics: Science and Systems XVI, 2020

2019

Learning to make decisions with unforeseen possibilities.

Craig Innes

PhD thesis, 2019

Learning Factored Markov Decision Processes with Unawareness.

Craig Innes, Alex Lascarides

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Structured Decision Problems with Unawareness.

Craig Innes, Alex Lascarides

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Reasoning about Unforeseen Possibilities During Policy Learning.

Craig Innes, Alex Lascarides, Stefano V. Albrecht, Subramanian Ramamoorthy, Benjamin Rosman

CoRR, 2018