1. 'Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes', presented at the 2022 Conference on Robot Learning (oral); 2. 'Coherent Soft Imitation Learning', presented at the 2023 Advances in Neural Information Processing Systems (NeurIPS) [spotlight]; 3. 'Stochastic Control as Approximate Input Inference', under review; 4. 'Neural Linear Models with Gaussian Process Priors', presented at the 2021 Advances in Approximate Bayesian Inference (AABI), co-first author with J. A. Lin; 5. 'Latent Derivative Bayesian Last Layer Networks', presented at the 2021 Artificial Intelligence and Statistics (AISTATS), co-first author with J. A. Lin; 6. 'A Differentiable Newton-Euler Algorithm for Real-World Robotics', submitted to IEEE Transactions on Robotics; 7. 'Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation', published in the IEEE Robotics and Automation Letters, Special Issue: Robotic Grasping and Manipulation Challenges and Progress.
Research Experience
1. Postdoctoral researcher at the Applied Artificial Intelligence Lab, Oxford Robotics Institute, University of Oxford, researching world models and robot learning methods; 2. Before starting his PhD, worked on developing Versius, a novel robotic system for laparoscopic surgery, from prototype to product; 3. Internship at Google DeepMind with the Robotics team, hosted by Sandy Huang and Nicholas Heess, during 2022-23.
Education
PhD: 2024 from TU Darmstadt, supervised by Prof. Jan Peters; Bachelor's: Information Engineering at Peterhouse, University of Cambridge, awarded the Charles Babbage senior scholarship.
Background
Research Interests: robotics, optimal control, approximate inference, system identification. Professional Field: robot and machine learning. Introduction: Currently a postdoctoral research assistant at the Oxford Robotics Institute, University of Oxford, working on world models and robot learning methods for sensorimotor manipulation.