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.

Soft robotics requires effective tools to educate the next generation of engineers and researchers. Stemming from a lack of universally accepted principles for education and with high barriers to entry in terms of fabrication and hardware, education to date has been highly ad hoc. We present a low-cost toolkit based on re-configurable balloon which allows rapid development of soft yet functional robots. This provides practical demonstrations of key soft robotic principles including: morphology, stiffness control, controller dependencies and modulation of environmental interactions, while grounding robot behaviours in fundamental mechani-cal models. We provide a framework for assembling balloon structures, incorporating actuation and exploring interactions. A diverse set of robots have been developed to show the potential to use this balloon-bots for educational activities for undergraduate teaching or below. In particular, different modes of locomotion are shown using robots each of which has an assembly time under 5 minutes. These robots can teach skills ranging from component integration and implementation, to key soft robotic design principles and embodied intelligence.

Soft sensorised skins are essential for improving robotic manipulation capabilities towards that of humans. Integration of sensors into existing robotic hands is challenging due to rigidity of components, low packing density or poor sensor response. We propose a sensorised skin, based-on barometric sensing, which can be molded over a skeletal robot hand. The sensors connect air chambers embedded in the soft skin to wrist-mounted pressure sensors, allowing sensor spacing 2–4 mm, force ranges from 23 mN to 5700 mN and bandwidth of 20 Hz. Integrating this with a skeletal hand allows us to showcase the potential of these sensors to aid robotic manipulation. We demonstrate 3-axis contact modelling, useful for in-hand manipulation and exploration. In addition, by grasping a chopstick and sensing forces transmitted from the environment, the system can remotely detect small environmental features, e.g., hole finding using tools.

Soft grippers have the potential to solve many existing manipulation challenges, particularly in agile industry applications. However, existing soft grippers are often limited in the range of objects they can pick, or by cluttered environments. We present a design inspired by the nose and fingers at the end of an elephant's trunk, which can pick both by suction and pinching, allowing increased grasping diversity. In addition, we observe an emergent grasping mode, a hybrid of pinching and suction where the cup aperture is morphed online, using embedded soft fingers, to form a seal over challenging objects. An algorithmic grasping strategy, based-on analytical grasping models and primitive objects, is presented. With this, we predict grasping performance and show increased grasping range compared to other soft gripper designs. Finally, the gripper and grasping strategy are successfully applied to grasping more varied everyday objects, demonstrating exploitation of this multi-modal gripping for adaptive grasping.

Prefabrication of structures is currently used in a limited capacity, due to the lack of flexibility, despite the potential cost and speed advantages. Autonomous flexible reassembly enables structures to be developed which can be continuously and iteratively dis-assembled and re-assembled providing far more flexibility in comparison to single shot pre-fabrication methods. Dis-assembly of structures should be considered when assembling, due to the asymmetry of assembly and dis-assembly processes, to ensure structures can be recycled and re-assembled. This allows for agile development, significantly reducing the time and resource usage during the build process. In this work, a framework for flexible re-assembly is developed and a robotic platform is developed to implement and test this framework with simple Lego bricks. The tradeoffs in terms of time, resource use and probability of success of this new assembly method can be understood by using a cost function to compare to alternative fabrication methods.