Welcome to the reading group presentation!
In this talk, Matias will explore a paper that introduces Bayesian Object Models (BOMs), a framework enabling robots to build rich, uncertainty-aware models of unseen objects from limited interaction. These models capture both structural and dynamic object properties using a differentiable probabilistic program. By combining a tree structure sampler with a physics engine, BOMs enable efficient gradient-based Bayesian inference, outperforming recent neural and physics-based alternatives.
Paper Link: Please find the relevant paper here.
Coming soon!
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Matias Mattamala is a Research Associate in Human-Robot Interaction.