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

TL;DR

This paper introduces a data-driven approach for autonomous plant manipulation, enabling robots to look behind foliage by predicting and executing effective actions to reveal hidden space, improving plant inspection and phenotyping tasks.

Contribution

The paper presents SRPNet, a neural network trained via self-supervision to predict revealed space from actions, and a method combining SRPNet with the cross-entropy method for effective exploration behind foliage.

Findings

SRPNet outperforms hand-crafted models in predicting space revealed.

The combined method effectively reveals hidden plant structures in real and synthetic settings.

The approach generalizes to novel plant configurations.

Abstract

Autonomous agriculture applications (e.g., inspection, phenotyping, plucking fruits) require manipulating the plant foliage to look behind the leaves and the branches. Partial visibility, extreme clutter, thin structures, and unknown geometry and dynamics for plants make such manipulation challenging. We tackle these challenges through data-driven methods. We use self-supervision to train SRPNet, a neural network that predicts what space is revealed on execution of a candidate action on a given plant. We use SRPNet with the cross-entropy method to predict actions that are effective at revealing space beneath plant foliage. Furthermore, as SRPNet does not just predict how much space is revealed but also where it is revealed, we can execute a sequence of actions that incrementally reveal more and more space beneath the plant foliage. We experiment with a synthetic (vines) and a real plant (Dracaena) on a physical test-bed across 5 settings including 2 settings that test generalization to novel plant configurations. Our experiments reveal the effectiveness of our overall method, PPG, over a competitive hand-crafted exploration method, and the effectiveness of SRPNet over a hand-crafted dynamics model and relevant ablations.