FruitTouch: A Perceptive Gripper for Gentle and Scalable Fruit Harvesting

Ruohan Zhang; Mohammad Amin Mirzaee; Wenzhen Yuan

cs.ROFeb 22, 2026

FruitTouch: A Perceptive Gripper for Gentle and Scalable Fruit Harvesting

Authors: Ruohan Zhang, Mohammad Amin Mirzaee, Wenzhen Yuan

Organizations: University of Illinois at Urbana-Champaign, Champaign, IL, USA

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.

Figures & tables

Fig. 1: Demonstration of the FruitTouch gripper harvesting cherry tomatoes. We designed the gripper for compactness, low cost, and scalability, while the perception system measures high-resolution contact geometry, 3D force, slip, and object softness. The design integrates mechanical efficiency with rich tactile sensing to enable reliable and efficient fruit harvesting.

Fig. 2: Mechanical and Optical Design of FruitTouch Gripper . (A) Mechanical Design. The gripper components are designed for scalability, enabling harvesting of fruits with varying sizes. We use gear-and-rack mechanism to provide actuation. Each finger consists of a soft silicone sensing surface supported by a transparent acrylic sheet, with three LED strips ensuring uniform illumination. A centrally mounted camera, in combination with mirrors, provides comprehensive coverage of both sensing surfaces. (B) Sensor Optical Design. The mirror configuration is optimized to maximize the sensing area while maintaining low distortion across different finger distances. Simulated outputs are shown for both the open and closed states of the gripper.

Fig. 3: Tactile Sensing Pipeline . (A) Contact-geometry reconstruction pipeline. Top: Raw camera output that contains reading from both fingers. (i) Unwarped contact frames on one sensor. (ii) Background-subtracted difference images. (iii) Estimated surface normals. (iv) Reconstructed 3D shape (shown in the form of a heightmap). (B) Example tactile images and the reconstructed shape for a cherry tomato, a strawberry, and a blackberry.

Fig. 4: (A) Softness Prediction Pipeline. We randomly segment tactile image sequences and the corresponding normal force signals from the pressing process of the same fruit, and extract embeddings using a shared backbone. A lightweight ranking head then predicts whether the test object is harder or softer than the reference. (B) Softness perception results. Each bar shows classification accuracy for a specific hardness within a fruit type (comparisons within the same fruit). The red dotted line marks random guess (50%).

Fig. 5: Setup and Results for Force Prediction. (A) Experimental setup with the gripper mounted on an ATI Nano Force/Torque sensor, which measures the ground-truth force. (B, C) Results of force estimation. The middle panel compares normal force ( $F_{n}$ ), while the right panel compares shear force ( $F_{\tau}$ ). For clarity, the magnitude of the shear force is used for visualization. The solid green line indicates the RANSAC fit, and the dotted red line denotes the identity line ( $y{=}x$ ).

Fig. 6: Setup and results of slip detection. (A) Two grasp positions from the experiment, illustrating different slip stages (stable → incipient slip → full slip). (B) Time‐series comparison of ground‐truth and predicted slip events. The y-axis shows the AprilTag displacement from the initial pose, which we use as the ground-truth slip signal. Predictions are overlaid for better visualization.

Fig. 7: Flow diagram of harvest experiment, including berry detection, initial pose calculation, and picking phases. (A) RGBD camera to scan the area and identify berries. (B) Align and move the manipulator to initial pose. (C) The gripper executes the picking action according to the chosen control strategy.

Cherry tomato
Fruit / Strategy	Succ. (%)	Attempts	$F_{z}$ Mean / Var ( $N$ , $N^{2}$ )
Open-loop	$58.3$	$-$	$-$
Slip control	$100$	$1.167$	$1.571/0.506$
Slip+Force control	$100$	$1.417$	$1.557/0.062$
Strawberry
Open-loop	$12.5$	$-$	$-$

TABLE I: Real Fruit Harvesting Results (Lab Setting)

Robotic Grasping

Robotic grasping studies how robots perceive, plan, and execute stable, task-appropriate contacts with objects across varied shapes and conditions.

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Apr 27, 2026J/K move · Enter open · S save

An analysis of sensor selection for fruit picking with suction-based grippers

Eva Krueger, Marcus Rosette, Joseph R. Davidson

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis OR 97331, USA

Robotic Grasping Tactile Sensing Agricultural Robotics Multimodal Sensor Fusion

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Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However, minimal sensor sets and phase-dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction-based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase-dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human-annotated ground truth.

Abstract

Robotic Grasping

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