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Abstract: Although agriculture is a highly mechanized industry, essential and high-value subsectors such as horticulture and floriculture remain heavily reliant on manual labor because they require complex, contact-rich, and highly selective handling of both plants and produce. The variability and density of tree-canopy clutter further complicate the automation process, making robot performance difficult to quantify [...]

Abstract: Real-world decision-making is rife with partial observability, long horizons, and complex multi-agent interactions. This thesis argues that abstraction—forming simplified representations of the task that retain relevant information—offers a unifying principle for tackling these challenges across model-free and model-based reinforcement learning (RL). We develop methods in which abstractions are not hand-designed but emerge from learning objectives, yielding representations that [...]