Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning

Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional rigid robots offer high accuracy and repeatability but lack the flexibility of soft robots. We argue that combining these characteristics in a hybrid robotic platform can significantly enhance overall capabilities. This work presents a novel hybrid robotic platform that integrates a rigid manipulator with a fully developed soft arm. This system is equipped with the intelligence necessary to perform flexible and generalizable tasks through imitation learning autonomously. The physical softness and machine learning enable our platform to achieve highly generalizable skills, while the rigid components ensure precision and repeatability.

Berry Twist: a Twisting-Tube Soft Robotic Gripper for Blackberry Harvesting

As global demand for fruits and vegetables continues to rise, the agricultural industry faces challenges in securing adequate labor. Robotic harvesting devices offer a promising solution to solve this issue. However, harvesting delicate fruits, notably blackberries, poses unique challenges due to their fragility. This study introduces and evaluates a prototype robotic gripper specifically designed for blackberry harvesting. The gripper features an innovative fabric tube mechanism employing motorized twisting action to gently envelop the fruit, ensuring uniform pressure application and minimizing damage. Three types of tubes were developed, varying in elasticity and compressibility using foam padding, spandex, and food-safe cotton cheesecloth. Performance testing focused on assessing each gripper's ability to detach and release blackberries, with emphasis on quantifying damage rates. Results indicate the proposed gripper achieved an 82% success rate in detaching blackberries and a 95% success rate in releasing them, showcasing the promised potential for robotic harvesting applications.