- Seiji Shaw, Travis Manderson, Chad Kessens, and Nicholas Roy, Toward Learning POMDPs Beyond Full-Rank Actions and State Observability [supplementary material]
- Aral Köse, Vadim Weinstein, and Steven LaValle, What a Fool Believes: Characterizing Plausible Environments from Weak Sensing Histories [supplementary material]
- Mohamad Louai Shehab, Antoine Aspeel, and Necmiye Ozay, Active Reward Machine Inference From Raw State Trajectories
- Da Kong and Vadim Indelman, Open-loop POMDP Simplification and Safe Skipping of Replanning with Formal Performance Guarantees [supplementary material]