Soft robotic grippers are intrinsically delicate while grasping objects, and can rely on mechanical deformation to adapt to different shapes without explicit control. These characteristics are particularly appealing for agriculture, where items of produce from the same crop can vary significantly in shape and size, and delicate harvesting is among the first concerns for fruit quality. Various soft robotic grippers have been proposed for harvesting different produce types, however their employment in field testing has been extremely limited. In this paper we developed the first closed structure soft gripper for the harvest of blackberries. We adapted an existing gripper concept, initially testing it on a sensorised raspberry physical twin. Then, followed grower-guided protocols to pick blackberries in farm polytunnels, and to evaluate the shelf life in comparison with berries picked by professional human pickers. Our results with ten experimental varieties showed a picking success rate of 95.4% demonstrating the capability of a closed structure gripper to adapt mechanically to fruit-shape variability. Moreover, a shelf life assessment on seven measured traits reported greatly improved shelf life of between 30 and 150%, across all traits for gripper harvested blackberries. Our study demonstrates the potential of soft grippers for delicate fruit harvesting, and indicates how to increase the impact of robotics in agriculture.

Learning manipulation from demonstration is a key way for humans to teach complex tasks. However, this domain mainly focuses on kinetic teaching, and does not consider imitation of interaction forces which is essential for more contact rich tasks. We propose a framework that enables robotic imitation of contact from human demonstration using a wearable finger-tip sensor. By developing a multi-modal sensor (providing both force and contact location) and robotic collection of simple training data of different motion primitives (tapping, rotation and translation), an LSTM-based model can be used to replicate motion from tactile demonstration only. To evaluate this approach, we explore the performance on increasingly complex testing data generated by a robot, and also demonstrate the full pipeline from human demonstration via the sensor used as a wearable device. This approach of using tactile sensing as a means of inferring the required robot motion paves the way for imitation of more contact-rich tasks, and enables imitation of tasks where the demonstration and imitation is performed with different body-schema.

Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex actions and also serves as a means of 'negative reinforcement’ to allow learning of poor actions or decision. Equipping robots with a reflex system and parallel learning architecture could help to prolong their useful life and allow for continued learning of safe actions. Focusing on a specific mock-up scenario of cubes on a 'stove’ like setup, we investigate the hardware and learning approaches for a robotic manipulator to learn the presence of 'hot’ objects and its contextual relationship to the environment. By creating a reflex arc using analog electronics that bypasses the 'brain’ of the system we show an increase in the speed of release by at least two-fold. In parallel we have a learning procedure which combines visual information of the scene with this 'pain signal’ to learn and predict when an object may be hot, utilizing an object detection neural network. Finally, we are able to extract the learned contextual information of the environment by introducing a method inspired by 'thought experiments' to generate heatmaps that indicate the probability of the environment being hot.