No abstract

Self-healing polymers can address the damage susceptibility in soft robotics. However, in most cases, their healing requires a heat stimulus, provided by an external device. This paper presents a self-healing soft actuator with an integrated healable flexible heater, functioning as the stimuliproviding system. The actuator is constructed out of thermoreversible elastomers that are crosslinked by the Diels-Alder (DA) reaction, which provides the healing ability. The heater is manufactured from a DA-based composite network filled with 20 wt% carbon black to provide electrically conductive properties for resistive Joule heating. The flexibility of the heater does not compromise the actuator performance upon integration and the self-healing properties of both heater and actuator allow for damage repair. This includes very large damages, as both heater and actuator can recover (near 100%) from being cut completely in two pieces, using Joule heating at 90 °C with a bias voltage of about 30 V. The embedded heater avoids the need for external intervention in the healing process, and provides healing quality assessment and a healing ondemand mechanism, paving the way for an optimum healing solution of damage resilient soft robots that require heat as a healing stimulus.

Natural agents display various adaptation strategies to damages, including damage assessment, localization, healing, and recalibration. This work investigates strategies by which a soft electronic skin can similarly preserve its sensitivity after multiple damages, combining material-level healing with software-level adaptation. Being manufactured entirely from selfhealing Diels-Alder matrix and composite fibers, the skin is capable of physically recovering from macroscopic damages. However, the simultaneous shifts in sensor fiber signals cannot be modelled using analytical approaches, since the materials viscoelasticity and healing processes introduce significant nonlinearities and time-variance into the skin's response. We show that machine learning of 5-layer networks after 5000 probes leads to highly sensitive models for touch localization with 2.3mm position and 95% depth accuracy. Through health monitoring via probing, damage and partial recovery are localized. Although healing is often successful, insufficient recontact leads to limited recovery or complete loss of a fiber. In these cases, complete resampling and retraining recovers the networks' full performance, regaining sensitivity and further increasing the system's robustness. Transfer learning with a single frozen layer provides the ability to rapidly adapt with fewer than 200 probes.