A Backdrivable Impedance‐Controlled Wrist for Safe and Measurable Canopy Interaction in Robotic Apple Harvesting

Unstructured orchard canopies present a major challenge for robotic fruit harvesting, where unpredictable branch contact and visually occluded foliage prevent reliable manipulation using vision alone. Rigid end‐effectors are vulnerable to this interaction, risking damage to gripper and canopy. Just as human pickers rely on physical compliance and tactile feedback to navigate dense canopies, this work presents a compact two‐degree‐of‐freedom backdrivable robotic wrist, the Transparent Robotic Agricultural Wrist (TRAW), designed to accommodate and estimate canopy interaction torques during apple harvesting. A capstan‐driven differential transmission achieves mechanical transparency through passive backdrivability, enabling open‐loop impedance control without passive compliant elements. A dynamic model incorporating gravity compensation enables real‐time sensorless interaction torque estimation from motor current. Benchtop experiments validate the stiffness rendering accuracy (RMS error 0.29 N m rad −1 across 2.00–20.00 N m rad −1 ) and torque tracking performance (RMS error 0.035 N m). Field trials in a commercial apple orchard evaluate wrist compliance during canopy approach and apple envelopment across 120 trials of varying difficulty, demonstrating that the TRAW safely accommodates canopy interaction while characterizing the interaction torques encountered. These results establish wrist‐level compliance with sensorless torque estimation as an effective approach for safe physical interaction in unstructured canopy environments.