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Autonomous agriculture applications (e.g., inspection, phenotyping, pluckingfruits) require manipulating the plant foliage to look behind the leaves andthe branches. Partial visibility, extreme clutter, thin structures, and unknowngeometry and dynamics for plants make such manipulation challenging. We tacklethese challenges through data-driven methods. We use self-supervision to trainSRPNet, a neural network that predicts what space is revealed on execution of acandidate action on a given plant. We use SRPNet with the cross-entropy methodto predict actions that are effective at revealing space beneath plant foliage.Furthermore, as SRPNet does not just predict how much space is revealed butalso where it is revealed, we can execute a sequence of actions thatincrementally reveal more and more space beneath the plant foliage. Weexperiment with a synthetic (vines) and a real plant (Dracaena) on a physicaltest-bed across 5 settings including 2 settings that test generalization tonovel plant configurations. Our experiments reveal the effectiveness of ouroverall method, PPG, over a competitive hand-crafted exploration method, andthe effectiveness of SRPNet over a hand-crafted dynamics model and relevantablations.

Push Past Green: Learning to Look Behind Plant Foliage by Moving It