Robotic Mushroom Harvesting with Real2Sim2Real and Model Predictive Path Integral (MPPI) Based Planning

Abstract

We present a strategy for te problem of robotic button mushroom harvesting (Agaricus Bisporus) that involves a Real2Sim2Real pipeline with dynamic scene reconstruction and a Model Predictive Path Integral (MPPI) control & planning architecture for generating optimal uprooting motion primitives based on a physics engine simulation framework. Given the complex, non-linear, anisotropic material properties of the mushrooms in combination with the multiple failure-mode modalities involved, we design a simulation framework around the PyBullet rigid-body-physics engine by utilizing first-order approximations of the equivalent continuum mechanics models. By exploiting the computational efficiency of the aforementioned simulation framework, we directly apply the MPPI control framework to generate offline optimal mushroom uprooting motion primitives, defining a set of cost objectives for an optimal and within-constraint harvesting plan. We show that with this planning strategy, the “root-bending” action emerges autonomously for the case of a single mushroom as an optimal uprooting maneuver, which corresponds well to empirical knowledge obtained by expert pickers. A video demonstration of the proposed architecture can be found in https://youtu.be/k38ePBsBego.