FruitTouch: A Perceptive Gripper for Gentle and Scalable Fruit Harvesting

2026-02-21

AI Summary

This work proposes a compact, perception-integrated gripper to address labor shortages in agricultural harvesting and the limitations of existing grippers, which struggle to stably grasp diverse fruits in confined spaces while providing reliable tactile feedback. The design innovatively embeds high-resolution visuotactile sensing directly into the mechanical structure, leveraging an embedded camera and an optimized optical system to enable high-fidelity tactile image acquisition and real-time analysis. Despite its simple and low-cost construction, the gripper supports force estimation, slip detection, and fruit softness prediction. Experimental validation on real-world fruit harvesting demonstrates its ability to gently and reliably grasp fruits of varying sizes, significantly improving harvesting efficiency while minimizing damage.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

The automation of fruit harvesting has gained increasing significance in response to rising labor shortages. A sensorized gripper is a key component of this process, which must be compact enough for confined spaces, able to stably grasp diverse fruits, and provide reliable feedback on fruit conditions for efficient harvesting. To address this need, we propose FruitTouch, a compact gripper that integrates high-resolution, vision-based tactile sensing through an optimized optical design. This configuration accommodates a wide range of fruit sizes while maintaining low cost and mechanical simplicity. Tactile images captured by an embedded camera provide rich information for real-time force estimation, slip detection, and softness prediction. We validate the gripper in real-world fruit harvesting experiments, demonstrating robust grasp stability and effective damage prevention.

Problem

• fruit harvesting
• perceptive gripper
• tactile sensing
• grasp stability
• automation

Innovation

• vision-based tactile sensing
• compact gripper
• force estimation
• slip detection
• fruit harvesting automation