Abstract
Precise pedicel-targeted grasping in strawberry harvesting remains a critical bottleneck due to the pedicel's small diameter (1.3-3 mm), color similarity with surrounding foliage, and precise force control required for clean abscission zone detachment. As a market-grade crop, strawberry aesthetic value makes bruise-free pedicel detachment essential over direct fruit grasping. Classical approaches struggle to unify vision models, motion planning, and contact manipulation into a robust framework, particularly in occluded canopy environments. Therefore, this work presents a visuomotor imitation learning pipeline for pedicel-targeted strawberry harvesting that unifies perception, motion planning, and contact manipulation from collecting human teleoperation demonstrations, training an Action Chunking with Transformers (ACT) policy with a ResNet-50 visual backbone, and deploying the learned policy autonomously on a Jetson AGX Orin edge platform at 15 Hz. The trained policy achieved consistent loss convergence from 2.84 to 0.091 over 100K gradient steps on 130 demonstrations. We have also established a reproducible open-source pipeline with publicly released dataset and model weights for the agricultural robotics community. Full dataset is available at, this https URL and trained policy at, this https URL. To replicate this work, all code, configurations, and deployment instructions are publicly available at, this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.22765
