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In order to progress the development of intelligent soft hands for general-purpose use in humanoid robots, social assistive robots, adaptive manufacturing, prosthetics and more, we need to rethink our approach to benchmarking. Where previously, hands are compared by their performance in a limited set of tasks, resulting in performance optimisations in the subjective, most common tasks. Instead, we must focus on increasing the hand’s potential at the lowest level, by improving the underlying passive behaviours, in terms of increased behavioural diversity and cheap control.

The ability to 3D print soft materials with integrated strain sensors enables significant flexibility for the design and fabrication of soft robots. Hydrogels provide an interesting alternative to traditional soft robot materials, allowing for more varied fabrication techniques. In this work, we investigate the 3D printing of a gelatin-glycerol hydrogel, where transglutaminase is used to catalyse the crosslinking of the hydrogel such that its material properties can be controlled for 3D printing. By including electron-conductive elements (aqueous carbon black) in the hydrogel we can create highly flexible and linear soft strain sensors. We present a first investigation into adapting a desktop 3D printer and optimizing its control parameters to fabricate sensorized 2D and 3D structures which can undergo >300% strain and show a response to strain which is highly linear and synchronous. To demonstrate the capabilities of this material and fabrication approach, we produce some example 2D and 3D structures and show their sensing capabilities. © 2016 IEEE.

Suction cups are widely used in pick and place tasks in well-structured and controlled environments with low variance in task requirements. For tasks requiring higher demands and variations, such as quality control of citrus fruits, commercial suction cups do not have the required reliability. The design and performance of a novel suction cup based on particle jamming is presented in this paper. Particle jamming allows operation in two states, a malleable state to adapt to large irregularities in object surfaces, and a stiff state to rigidly hold the object for manipulation and prevent the cup collapsing in on itself. This jamming cup is shown to successfully adapt to surfaces with up to 7 times variations compared to standard suction cups. Additionally, for similar scale cups, the holding force is comparable and has

The ability to sense compliant interactions through internal state measurements improves adaptability and robustness of robotic grasping and manipulation, without interfering with any carefully designed passive dynamics. This work presents a collocated sensing and actuation system using a compliant tendon driven hand and its accompanying model. Using a simple vision-based measurement device the approach is able to estimate its internal state and applied external forces. The camera-based sensor uses off-the-shelf components and has the ability to measure many more than 15 tendons simultaneously, giving a low-cost, scale-able, high-bandwidth sensing solution. The collocated configuration provides a framework for developing stable feedback controllers. Our results show that the system can estimate change in posture with $\lt 10$% error with the potential to estimate contact forces using the same scheme.

Robotic manufacturing is increasingly moving towards one-off assembly tasks. Agile manufacturing systems must be able to adapt to changing parts and designs. The addition of a soft interface, between a picking tool and a part, can improve the performance and introduce new ways of picking and manipulating parts without increasing complexity. The introduced compliance also assists when mating parts. These soft interfaces can be applied in minimalist pick and place tasks with passive gripping. This work investigates the model behind gel-based soft interfaces, and demonstrates the application for manipulating fasteners in an agile assembly task. The model predicts the limit to the holding force is from a vacuum seal, the actual holding force can be lower due to the fluid properties of the gel in confined geometries. The soft interface is shown to give adaptability for a single tool to pick multiple different sizes of parts. This interface gives success rate > 90% for well aligned cross-head bolts, and much better performance for hex keys and bolts. © 2019 IEEE.

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. © 2018 IEEE.

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors’ knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 h to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task. © 2020, Springer Nature Switzerland AG.