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=== Actuation ===
Actuation in robotic systems allows the robot to move. Adaptable [[Actuator|actuators]] typically function in response to environmental changes, such as changes in temperature which may change the shape of the actuator. Thus, altering functionality.<ref>{{Cite web |title=Actuators: what is it, definition, types and how does it work |url=https://www.progressiveautomations.com/pages/actuators |access-date=2023-11-09 |website=Progressive Automations |language=en}}</ref> Self-powering (untethered) actuation is achievable, especially in soft robotics where external stimuli can change the shape of an actuator, creating mechanical energy.<ref name=":22">
==Software==
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=== Soft Robots ===
Robotics with soft grippers is an emerging field in the adaptable robotic scene which is based on the [[Venus flytrap]]. Two soft robotic surfaces provide enveloping and pinching grasp modules. This technology is tested in a variety of environments to determine the effects of diverse objects, errors of object position, and soft robotic surface installation on grasping capacity.<ref>{{
=== Modular Robots ===
Robots designed for the outdoors that adapt to changing landscapes and obstacles. These are constructed like a chain of individual modules with simple hinge joints, enabling modular robots to morph themselves into various shapes to traverse terrain. Some of these forms include configurations like [[spider]], serpentine, and loop.<ref>{{
=== Swarm Robotics ===
Field of robotics utilizing swarm intelligence to groups of simple homogeneous robots. Swarm robots follow algorithms, usually designed to mimic the behavior of real animals, in order to determine their movements in response to environmental stimuli.
=== Biohybrid Robots ===
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Adaptable robotics possess capabilities that have made them applicable to many fields including, but not limited to, the medical, industrial, and experimental fields.
[[Reinforcement learning|Learning from demonstration]] is a strategy for transferring human motion skills to robots. The primary goal is to identify significant movement primitives, significant movements humans make, from demonstration and remake these motions to adapt the robot to that motion. There have been a few issues with robots being unable to adapt skills learned by learning from demonstration to new environments (a change from the scenario in which the robot was given initial demonstrations). These issues with learning from demonstration have been addressed with a learning model based on a nonlinear dynamic system which encodes trajectories as dynamic motion primitive, which are similar to movement primitives, but they are significant movements represented by a mathematical equation; equation variables change with the changing environment, altering the motion performed. The trajectories recorded through these systems have proven to apply to a wide variety of environments making the robots more effective in their respective spheres. Learning from demonstration has progressed the applicability of robotics in fields where precision is essential, such as surgical environments.<ref name=":5">{{
In the medical field, SAR technology focuses on taking sensory data from wearable peripherals to perceive the user’s state of being. The information gathered enables the machine to provide personalized monitoring, motivation, and coaching for rehabilitation. Intuitive Physical HRI and interfaces between humans and robots allow functionalities like recording the motions of a surgeon to infer their intent, determining the mechanical parameters of human tissue, and other sensory data to use in medical scenarios.<ref name=":6">{{
AI, machine learning, and deep learning have allowed advances in adaptable robotics such as autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies have been essential in the development of cobots (collaborative robots), which are robots capable of working alongside humans capable of adapting to changing environments.<ref name=":7">{{
In the industrial field, AI, Machine Learning, and Deep Learning can be used to perform quality control checks on manufactured products, identify defects in products, and alert production teams to make necessary changes in real-time.<ref name=":7" />
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