Load-bearing hydrogels ionically reinforced through competitive ligand exchanges

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Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

Novel technologies, fabrication methods, controllers and computational methods are rapidly advancing the capabilities of soft robotics. This is creating the need for design techniques and methodologies that are suited for the multi-disciplinary nature of s ...

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Polydimethylsiloxane (PDMS) based soft polymer optical fibers (POFs) have recently demonstrated their intriguing potential for different sensing applications like strain or temperature sensing. Recent studies have elaborated on the sensing capability of PD ...

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Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system' ...

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The soluble fraction of atmospheric transition metals is particularly associated with health effects such as reactive oxygen species compared to total metals. However, direct measurements of the soluble fraction are restricted to sampling and detection uni ...

Shape-changing robots adapt their own morphology to address a wider range of functions or environments than is possible with a fixed or rigid structure. Akin to biological organisms, the ability to alter shape or configuration emerges from the underlying m ...

Stretchable and conductive nanocomposites are emerging as important constituents of soft mechanical sensors for health monitoring, human-machine interactions, and soft robotics. However, tuning the materials' properties and sensor structures to the targete ...

Load-bearing hydrogels ionically reinforced through competitive ligand exchanges

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