Robotic Fruits with Tunable Stiffness and Sensing: Towards a Methodology for Developing Realistic Physical Twins of Fruits

TL;DR

This paper introduces a methodology for creating tunable soft physical twins of fruits, enabling realistic simulation of fruit stiffness for robotic harvesting without extensive use of real produce.

Contribution

It presents a novel fiber-reinforced pneumatic physical twin of a kiwi fruit with adjustable stiffness, improving benchmarking and training of robotic grippers in agriculture.

Findings

Stiffness tuning accuracy over 97%

Sensor feedback accurately reflects gripping forces

Reliable maintenance of desired stiffness over 50 cycles

Abstract

The global agri-food sector faces increasing challenges from labour shortages, high consumer demand, and supply-chain disruptions, resulting in substantial losses of unharvested produce. Robotic harvesting has emerged as a promising alternative; however, evaluating and training soft grippers for delicate fruits remains difficult due to the highly variable mechanical properties of natural produce. This makes it difficult to establish reliable benchmarks or data-driven control strategies. Existing testing practices rely on large quantities of real fruit to capture this variability, leading to inefficiency, higher costs, and waste. The methodology presented in this work aims to address these limitations by developing tunable soft physical twins that emulate the stiffness characteristics of real fruits at different ripeness levels. A fiber-reinforced pneumatic physical twin of a kiwi fruit was designed and fabricated to replicate the stiffness at different ripeness levels. Experimental results show that the stiffness of the physical twin can be tuned accurately over multiple trials (97.35 - 99.43% accuracy). Gripping tasks with a commercial robotic gripper showed that sensor feedback from the physical twin can reflect the applied gripping forces. Finally, a stress test was performed over 50 cycles showed reliable maintenance of desired stiffness (0.56 - 1.10% error). This work shows promise that robotic physical twins could adjust their stiffness to resemble that of real fruits. This can provide a sustainable, controllable platform for benchmarking and training robotic grippers.