6 accepted papers
Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system that leverages imitation learning for autonomous pepper harvesting designed to operate in these complex s
Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between syn-thetic and real-world visual data. In this paper, we propose SplatSim, a novel framework that leverages Gaussian Splatting as the
Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabiliti…
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired outdoors for such tasks is changing lighting conditions that can alter the appearance of the objects…
We outline a neural network-based pipeline for perception, control and planning of a 7 DoF robot for tasks that involve reaching into a dormant grapevine canopy. The proposed system consists of a 6 DoF industrial robot arm and a linear slider that can actuate on an entire grape vine. Our approach us…
There are no mechanical harvesters for the fresh market apple industry commercially available. The absence of automated harvesting technology is a critical problem because of rising production costs and increasing uncertainty about future labor availability. This paper presents the preliminary desig…