Bioinspired material research aims at learning from the sophisticated design principles of nature, in order to develop novel artificial materials with advanced functionalities. Some of the sophisticated capabilities of biological materials, such as their ability to self-heal or adapt to environmental changes, are challenging to realize in artificial systems. Nevertheless, many efforts have been recently devoted to develop artificial materials with adaptive functions, especially materials which can generate movement in response to external stimuli. One such effort is the field of soft robots, which aims towards fabrication of autonomous adaptive systems with flexibility, beyond the current capability of conventional robotics. However, in most cases, soft robots still need to be connected to hard electronics for powering and rely on complicated algorithms to control their deformation modes. Soft robots that can be powered remotely and are capable of self-regulating function, are of great interest across the scientific community.In order to realize such responsive and adaptive systems, researches across the globe are making constant efforts to develop new, ever-more sophisticated stimuliresponsive materials. Among the different stimuli-responsive materials, liquid crystal networks (LCNs) are the most suited ones to design smart actuating systems as they can be controlled and powered remotely with light and thereby obviate the need for external control circuitry. They enable pre-programable shape changes, hence equipping a single material with multiple actuation modes. In addition to light, they can also be actuated by variety of stimuli such as heat, humidity, pH, electric and magnetic fields etc., or a combination of these. Based on these advantages of LCNs, we seek inspiration from natural actuator systems present in plants and animals to devise different light controllable soft robotic systems.In this thesis, inspired from biological systems such as octopus arm movements, iris movements in eyes, object detection and capturing ability of Venus flytraps and opening and closing of certain nocturnal flowers, we demonstrate several light robots that can be programmed to show pre-determined shape changes. By employing a proper device design, these light robots can even show the characteristics of selfregulation and object recognition, which brings new advances to the field of LCNbased light robots. For instance, octopod light robot can show bidirectional bending owing to alignment programming using a commercial laser projector; artificial iris is a fully light controllable device that can self-regulate its aperture size based on intensity of incident light; the optical flytrap can not only autonomously close on an object coming into its ‘‘mouth’’ but it can also distinguish between different kinds of objects based on optical feedback, and finally, integration of light and humidity responsiveness in a single LCN actuator enables a nocturnal flower-mimicking actuator, which provides an opportunity to understand the delicate interplay between different simultaneously occurring stimuli in a monolithic actuator.We believe that besides providing a deeper understanding on the photoactuation in liquid crystal networks, at fundamental level, our work opens new avenues by providing several pathways towards next-generation intelligent soft microrobots
