Adaptable robotics
Adaptable robotics are generally based in robot developer kits. This technology is distinguished from static automation due to its capacity to adapt to changing environmental conditions and material features while retaining a degree of predictability required for collaboration (e.g. human-robot collaboration). The degree of adaptability is demonstrated in the way these can be moved around and used in different tasks. Unlike static or factory robots, which have pre-defined way of operating, adaptable robots can function even if a component breaks, making them useful in cases like caring for the elderly, doing household tasks, and rescue work. Adaptable Robotics systems successfully adapt to their environment using techniques such as modular design, machine learning, and sensor feedback. Using this, they have revolutionized various industries and have the ability to address many real-world challenges. Software
The kits come with an open software platform tailored to a range of common robotic functions. The kits also come with common robotics hardware that connects easily with the software (infrared sensors, motors, microphone and video camera), which add to the capabilities of the robot. The process of modifying a robot to achieve varying capabilities such as collaboration could merely include the selection of a module, the exchange of modules, robotic instruction via software, and execution. Types of Adaptable Robots

Soft Robots 
Robotics with soft grippers is an emerging field in the adaptable robotic scene which is based off of the Venus fly trap. 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 SRS (Soft Robotic Surface) installation on grasping capacity. Applications of Adaptable Robotics

Learning from demonstration (LfD) 
Learning from demonstration (LfD) is a strategy for transferring human motion skills to robots. The primary goal of this is to identify significant movement primitives (MPs) from human demonstration and remake these motions to adapt the robot to new situations. Issues with LfD have been addressed with a learning model based on a nonlinear dynamic system (DS) which encodes trajectories as dynamic motion primitive (DMP). The trajectories recorded through these systems have proven to be applicable to a wide variety of environments making the robots more effective in their respective spheres when using this adaptable robotic technology. SAR 
In the medical field, SAR technology focuses on taking sensory data from wearable peripherals in order 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. Challenges and Limitations 
Systems which involve physical collaboration between humans and robots are difficult to design well due to human uncertainty. Humans alter the force of their motions regularly due to human factors like emotion, biological processes, and other extraneous factors unknown to a robot. This can make sensory data difficult to quantify for successful adaptation in robots. Furthermore, the specific needs, characteristics, and preferences to which a patient in a medical scenario may need vary from person to person. Adaptable robotic systems need extended time to adapt to the new environment introduced from patient to patient. See also
 Domestic robot
 Terrainability

References

Robot kits
Adaptable robotics