These are my papers, preprints and thesis. My current work is on how teams of robots learn to work together, using reinforcement learning and game theory.
If you can’t get hold of a paper, email me and I’ll send you a copy. I’m also happy to talk about research or collaboration, so do reach out!
Preprints & Under Review
RL²-VLA: Adaptive RL Latent Compositional Steering with Test-Time Scaling for Vision-Language-Action Models
2026 Preprint
An inference-time steering framework that composes a lightweight offline RL policy with a frozen vision-language-action model, activating only when the base policy is likely to fail. On the SIMPLER and PolaRiS benchmarks, out-of-domain success rates improve by up to 17.3 percentage points.
Workshop Papers
These workshops don't publish proceedings, so please cite the arXiv version.
Pairwise Approximation Can Select the Wrong Multi-Robot Plan
IROS 2026 Workshop on Intelligent Information Gathering, 2026
Many multi-robot coordination methods score a joint plan using only terms for single robots and pairs of robots. Replaying every subset of recorded four-robot exploration plans shows that the pairwise score picks a different plan from the exact score on six of seven maps at the 15 m range, with a regret of up to 0.337 of map coverage.
Thesis
A natural language processing approach to improve demand forecasting in long supply chains
Master of Engineering Thesis, Massachusetts Institute of Technology, 2020
A deep learning model (NEMO) that uses natural language processing signals from public text to forecast commodity demand in long supply chains, where information sharing across firms is rarely feasible.