Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence.
A traditional problem in robotics is adaptation of developed algorithms to different platforms and sensors, as each of them has its specifics and associated errors.
Compare this liveliness to the robotic 2007 film adaptation in which John Travolta is covered in foam-rubber as a pseudo-Divine who looks more like the mechanical shark in Jaws than anything living.
More dynamic cooperation between cities, businesses and governments enables new solutions to emerging global challenges such as climate change adaptation, robotic innovation, cyber-security, and increasing migration.
Previous laparoscopic experience has a complex influence on the physical and mental adaptation to robotic surgery.
The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation.
Our robotic platforms include mechanical adaptations and the development of an embedded software architecture.
The objective of this work, therefore, was to develop a robotic assistant capable of motion-level adaptation and to evaluate through human subject experimentation whether this type of adaptation leads to more efficient teamwork and a more satisfied human coworker.
The small-scale and high precision of the method could allow for automation of the procedure and adaptation to a robotics platform.
A more likely scenario for achieving artificial intelligence within our lifetime is through a network of sub programs handling vision (computer vision), language (NLP), adaptation (machine learning), movement (robotics)…etc.
