Craig Innes

Introduction / Motivation

I'm currently working as a postdoctoral researcher in the UoE Robust Autonomy and Decisions Group (RAD) with Subramanian Ramamoorthy. I'm interested in safety guarantees and risk assessment of Robotic-AI. In particular - how can we combine traditional models of symbolic-logic and formal verification, with newer black-box probabilistic models seen in modern complex machine learning systems?

I did my PhD at the Edinburgh Institute for Language, Cognition and Computation (ILCC), which was all about the semantics of unawarness (or "unknown unknowns"). You can read it here if you like.

In my spare time I like to design small games. You can find a list of them on my Itch.io page. If you're interested in making games, I suggest using Godot then joining a Game Jam to get inspired.

Related Work

Conference Papers

  • Innes, C., & Ramamoorthy, S. (2023). Testing rare downstream safety violations via upstream adaptive sampling of perception error models. In International Conference on Robotics and Automation (ICRA)
    paper
  • Lahariya, M., & Innes, C., & Develder, C., & Ramamoorthy, S. (2022). Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators. In International Conference on Intelligent Robots and Systems (IROS)
    paper
  • Corso, A., & Katz, S., & Innes, C., & Du, X., & Ramamoorthy, S., & Kochenderfer, M. (2022). Risk-driven design of perception systems. In Conference on Neural Information Processing Systems (NeurIPS)
    paper
  • Innes, C., & Ramamoorthy, S. (2022). Automated testing with temporal logic specifications for robotic controllers using adaptive experiment design. In International Conference on Robotics and Automation (ICRA)
    paper
  • Viano, L., & Huang, Y., & Kamalaruban, P., & Innes, C., & Ramamoorthy, S., & Weller, A. (2022). Robust Learning from Observation with Model Misspecification. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
  • Innes, C., & Ramamoorthy, S. (2021). ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments. In International Conference on Robotics and Automation
    paper
  • Innes, C., & Ramamoorthy, S. (2020). Elaborating on Learned Demonstrations with Temporal Logic Specifications. In Robotics Science and Systems
    paper technical-supplement
  • Innes, C., & Lascarides, A. (2019). Learning Factored Markov Decision Processes with Unawareness. In Uncertainty in Artificial Intelligence
    paper technical-supplement
  • Innes, C., & Lascarides, A. (2019). Learning Structured Decision Problems with Unawareness. In International Conference on Machine Learning
    paper technical-supplement

Journal Papers

  • Burke, M., & Lu, K., & Angelov, D., & Straižys, A., & Innes, C., & Subr, K., & Ramamoorthy, S. (2021). Learning robotic ultrasound scanning using probabilistic temporal ranking. In (Working paper)
    paper

Workshop Papers and Extended Abstracts

  • Innes, C., & Hristov, Y., & Kamaras, G., & Ramamoorthy, S. (2021). Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications . In 10th Workshop on Synthesis (SYNT) at the International Conference on Computer Aided Verification (CAV)
    paper
  • Innes, C., & Lascarides, A. (2019). Learning Factored Markov Decision Processes with Unawareness - Extended Abstract . In International Conference on Autonomous Agents and Multiagent Systems
    paper

References

You can email me at [my-first-name] [dot] [my-last-name] [at] ed [dot] ac [dot] uk