Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.

With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand-tuned signals. Here, a learning-based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim-to-real transfer is presented. Developing a data-driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback-Leibler divergence-based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.

Single-step precision programming of decoupled multiresponsive soft millirobots.

Stimuli-responsive soft robots offer new capabilities for the fields of medical and rehabilitation robotics, artificial intelligence, and soft electronics. Precisely programming the shape morphing and decoupling the multiresponsiveness of such robots is crucial to enable them with ample degrees of freedom and multifunctionality, while ensuring high fabrication accuracy. However, current designs featuring coupled multiresponsiveness or intricate assembly processes face limitations in executing complex transformations and suffer from a lack of precision. Therefore, we propose a one-stepped strategy to program multistep shape-morphing soft millirobots (MSSMs) in response to decoupled environmental stimuli. Our approach involves employing a multilayered elastomer and laser scanning technology to selectively process the structure of MSSMs, achieving a minimum machining precision of 30 µm. The resulting MSSMs are capable of imitating the shape morphing of plants and hand gestures and resemble kirigami, pop-up, and bistable structures. The decoupled multistimuli responsiveness of the MSSMs allows them to conduct shape morphing during locomotion, perform logic circuit control, and remotely repair circuits in response to humidity, temperature, and magnetic field. This strategy presents a paradigm for the effective design and fabrication of untethered soft miniature robots with physical intelligence, advancing the decoupled multiresponsive materials through modular tailoring of robotic body structures and properties to suit specific applications.

Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation.

Bioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual-free, repeatable, self-cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance. Previous fibril designs for shear optimization are limited by predefined standard shapes in a narrow range primarily based on human intuition, which restricts their maximum performance. This study combines the machine learning-based optimization and finite-element-method-based shear mechanics simulations to find shear-optimized fibril designs automatically. In addition, fabrication limitations are integrated into the simulations to have more experimentally relevant results. The computationally discovered shear-optimized structures are fabricated, experimentally validated, and compared with the simulations. The results show that the computed shear-optimized fibrils perform better than the predefined standard fibril designs. This design optimization method can be used in future real-world shear-based gripping or nonslip surface applications, such as robotic pick-and-place grippers, climbing robots, gloves, electronic devices, and medical and wearable devices.

Size-Dependent Locomotion Ability of Surface Microrollers on Physiologically Relevant Microtopographical Surfaces.

Controlled microrobotic navigation inside the body possesses significant potential for various biomedical engineering applications. Successful application requires considering imaging, control, and biocompatibility. Interaction with biological environments is also a crucial factor in ensuring safe application, but can also pose counterintuitive hydrodynamic barriers, limiting the use of microrobots. Surface rolling microrobots or surface microrollers is a robust microrobotic platform with significant potential for various applications; however, conventional spherical microrollers have limited locomotion ability over biological surfaces due to microtopography effects resulting from cell microtopography in the size range of 2-5 µm. Here, the impact of the microtopography effect on spherical microrollers of different sizes (5, 10, 25, and 50 µm) is investigated using computational fluid dynamics simulations and experiments. Simulations revealed that the microtopography effect becomes insignificant for increasing microroller sizes, such as 50 µm. Moreover, it is demonstrated that 50 µm microrollers exhibited smooth locomotion ability on in vitro cell layers and inside blood vessels of a chicken embryo model. These findings offer rational design principles for surface microrollers for their potential practical biomedical applications.

Electrodeposited Superhydrophilic-Superhydrophobic Composites for Untethered Multi-Stimuli-Responsive Soft Millirobots.

To navigate in complex and unstructured real-world environments, soft miniature robots need to possess multiple functions, including autonomous environmental sensing, self-adaptation, and multimodal locomotion. However, to achieve multifunctionality, artificial soft robots should respond to multiple stimuli, which can be achieved by multimaterial integration using facile and flexible fabrication methods. Here, a multimaterial integration strategy for fabricating soft millirobots that uses electrodeposition to integrate two inherently non-adherable materials, superhydrophilic hydrogels and superhydrophobic elastomers, together via gel roots is proposed. This approach enables the authors to electrodeposit sodium alginate hydrogel onto a laser-induced graphene-coated elastomer, which can then be laser cut into various shapes to function as multi-stimuli-responsive soft robots (MSRs). Each MSR can respond to six different stimuli to autonomously transform their shapes, and mimic flowers, vines, mimosas, and flytraps. It is demonstrated that MSRs can climb slopes, switch locomotion modes, self-adapt between air-liquid environments, and transport cargo between different environments. This multimaterial integration strategy enables creating untethered soft millirobots that have multifunctionality, such as environmental sensing, self-propulsion, and self-adaptation, paving the way for their future operation in complex real-world environments.

Programmable aniso-electrodeposited modular hydrogel microrobots.

Systems with programmable and complex shape morphing are highly desired in many fields wherein sensing, actuation, and manipulation must be performed. Living organisms use nonuniform distributions of their body structural composition to achieve diverse shape morphing, motion, and functionality. However, for the microrobot fabrication, these designs often involve complicated robotic architectures requiring time-consuming and arduous fabrication processes. This paper proposes a single-step aniso-electrodeposition method for fabricating modular microrobots (MMRs) with distinct functions in each modular segment. By programming the electric field, the microscale stripe-shaped structure can be endowed with diverse shape-morphing capabilities, such as spiraling, twisting, bending, and coiling. The proposed fabrication method can develop MMRs with multiple independent modules onto which cells, drugs, and magnetic nanoparticles can be loaded to achieve multifunctionality. Thus, MMRs can perform multiple tasks, such as propulsion, grasping, and object delivery, simultaneously under magnetic control and ionic and pH stimuli.