Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm

Key points

• As a market-grade crop, strawberry aesthetic value makes bruise-free pedicel detachment essential over direct fruit grasping.1
• Classical approaches struggle to unify vision models, motion planning, and contact manipulation into a robust framework, particularly in occluded canopy environments.1
• 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.1
• We have also established a reproducible open-source pipeline with publicly released dataset and model weights for the agricultural robotics community.1

Sources (1)

• [1]Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 12:11 AM

  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.

Extractive summary: sentences quoted from the sources.