[3] John Adler’s invention in 1994, the cyberknife, is a robotic surgery system that is capable of using ultra-fine precision in medical procedures which demonstrates such adaptations.
AI systems can process this data and adjust task primitives accordingly, leading to adapted action.
Later that year, IBM’s Deep Blue computer defeated Garry Kasparov in a game of chess, a landmark for robotic AI’s ability to plan and react.
[9] Untethered actuation is achievable, especially in soft robots with liquid crystal polymers, a category of stimuli-responsive materials with two way shape memory effect.
This can allow the liquid crystal polymers to generate mechanical energy by changing shape in response to external stimuli, hence untethered actuation.
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.
Swarm robots follow algorithms, usually designed to mimic the behavior of real animals, in order to determine their movements in response to environmental stimuli.
[12] Biohybrid robotics use living tissues or cells to provide machines with functions that would be difficult to achieve otherwise.
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.
[19] In the medical field, SAR technology focuses on taking sensory data from wearable peripherals to perceive the user’s state of being.
With AI models becoming rapidly more advanced, the need to develop peripheral technologies able to provide necessary information for these systems becomes increasingly more challenging.
[12] Biohybrid robotics have challenges with living cells being delicate even though they are adaptable to a variety of environments due to the properties of the biological material.