Marc Rigter

Welcome to my personal website!

I’m currently a researcher at Microsoft Research in the UK, working on foundation models for decision-making and embodied AI.

Previously, I was a postdoc with Prof. Ingmar Posner at the Applied AI Lab at the Oxford Robotics Institute (ORI) at the University of Oxford. I completed my PhD in machine learning and robotics at the GOALS Group at the ORI, advised by Prof. Nick Hawes and Dr. Bruno Lacerda. At Oxford, my research focused on generative world models, deep reinforcement learning, and planning under uncertainty.

Selected Publications

ICLR

Reward-free curricula for training robust world models

Marc Rigter , Minqi Jiang , and Ingmar Posner

International Conference on Learning Representations, 2024

Bib PDF Code

@article{rigter2024reward,
  title = {Reward-free curricula for training robust world models},
  author = {Rigter, Marc and Jiang, Minqi and Posner, Ingmar},
  journal = {International Conference on Learning Representations},
  year = {2024},
}

TMLR

World models via policy-guided trajectory diffusion

Marc Rigter , Jun Yamada , and Ingmar Posner

Transactions on Machine Learning Research, 2024

Bib PDF Code

@article{rigter2023world,
  title = {World models via policy-guided trajectory diffusion},
  author = {Rigter, Marc and Yamada, Jun and Posner, Ingmar},
  journal = {Transactions on Machine Learning Research},
  year = {2024},
}

NeurIPS

One risk to rule them all: A risk-sensitive perspective on model-based offline reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2023

Bib PDF Code

@article{rigter2023one,
  title = {One risk to rule them all: {A} risk-sensitive perspective on model-based offline reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2023},
}

NeurIPS

RAMBO-RL: Robust adversarial model-based offline reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2022

Bib PDF Code

@article{rigter2022rambo,
  title = {{RAMBO-RL}: Robust adversarial model-based offline reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2022},
}

AAAI

Optimal admission control for multiclass queues with time-varying arrival rates via state abstraction

Marc Rigter , Danial Dervovic , Parisa Hassanzadeh , and 3 more authors

AAAI Conference on Artificial Intelligence, 2022

Bib PDF

@article{rigter2022optimal,
  title = {Optimal admission control for multiclass queues with time-varying arrival rates via state abstraction},
  author = {Rigter, Marc and Dervovic, Danial and Hassanzadeh, Parisa and Long, Jason and Zehtabi, Parisa and Magazzeni, Daniele},
  journal = {AAAI Conference on Artificial Intelligence},
  year = {2022},
}

ICAPS

Planning for risk-aversion and expected value in MDPs

Marc Rigter , Paul Duckworth , Bruno Lacerda , and 1 more author

International Conference on Automated Planning and Scheduling, 2022

Bib PDF Code

@article{rigter2022planning,
  title = {Planning for risk-aversion and expected value in MDPs},
  author = {Rigter, Marc and Duckworth, Paul and Lacerda, Bruno and Hawes, Nick},
  journal = {International Conference on Automated Planning and Scheduling},
  year = {2022},
}

NeurIPS

Risk-averse Bayes-adaptive reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2021

Bib PDF

@article{rigter2021risk,
  title = {Risk-averse {B}ayes-adaptive reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2021},
}

AAAI

Minimax regret optimisation for robust planning in uncertain Markov decision processes

Marc Rigter , Bruno Lacerda , and Nick Hawes

AAAI Conference on Artificial Intelligence, 2021

Bib PDF

@article{rigter2021minimax,
  title = {Minimax regret optimisation for robust planning in uncertain Markov decision processes},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {AAAI Conference on Artificial Intelligence},
  year = {2021},
}

RAL

A framework for learning from demonstration with minimal human effort

Marc Rigter , Bruno Lacerda , and Nick Hawes

IEEE Robotics and Automation Letters, 2020

Bib PDF

@article{rigter2020framework,
  title = {A framework for learning from demonstration with minimal human effort},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {IEEE Robotics and Automation Letters},
  year = {2020},
}

IROS

An autonomous quadrotor system for robust high-speed flight through cluttered environments without GPS

Marc Rigter , Benjamin Morrell , Robert G Reid , and 5 more authors

International Conference on Intelligent Robots and Systems, 2019

Bib PDF

@article{rigter2019autonomous,
  title = {An autonomous quadrotor system for robust high-speed flight through cluttered environments without GPS},
  author = {Rigter, Marc and Morrell, Benjamin and Reid, Robert G and Merewether, Gene B and Tzanetos, Theodore and Rajur, Vinay and Wong, KC and Matthies, Larry H},
  journal = {International Conference on Intelligent Robots and Systems},
  year = {2019},
}