Escalating Robotics Applications through Integration of AI & ML
Published on : Friday 03-07-2020
Currently, robotic technology witnesses a wide number of investments to escalating its applications across the industries in the global market, which makes it one of the interesting areas for developers, industry analysts, and startups to focus on. In the market, innovators and entrepreneurs are expanding their capabilities through innovation and development in emerging technologies such as artificial intelligence (AI) and machine learning (ML).
Three Types of AI that Uses in Robotic Application
Companies are aiming robotic technology as a business opportunity rather than just as a technology. Moreover, the integration of AI in the robots can further support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.
Process Automation – the automation to execute different digital and physical tasks, typically back-office administrative and financial activities through the robotic process automation technologies. For instance, NASA implemented RPA on a four-pillar theory i.e. in accounts payable and receivable, IT spending, and human resources, all managed by a shared services center.
Cognitive Insight – The AI algorithms developed to detect patterns in vast volumes of data and interpret their meaning. It is used to make predictions based on the large data-sets of the companies to identify consumer buying patterns and real-time insurance fraud, along with the more accurate and detailed actuarial modeling.
Cognitive Engagement – Projects based on the engagement of employees and consumers using AI technologies such as natural language processing chatbots, intelligent agents, and machine learning. It is used to offers 24/7 customer support to enhance engagement with the consumers and the companies.
Key five Machine Learning (ML) Applications in Robotics
Computer Vision – It is also called machine vision, also it processes physical data and continuous updates in the robot guidance and automatic inspection systems. It also uses extrasensory technologies such as radar, lidar, and ultrasound to execute operation through autonomous vehicles and drones based 360-degree vision-based systems.
Imitation Learning – This approach is based on real-time observational learning, a behavior exhibited by newborns and children.
Self-Supervised Learning – This approach enables the robots to generate their own training examples in order to improve performance in terms of detecting and reject objects.
Assistive and Medical Technologies – An assistive robot in the medical sector is a device that uses to sense, process sensory information, and perform actions to assist the patients in terms of serving food, drugs, and personal bringing in the hospitals and clinics.
Multi-Agent Learning – It is based on coordination and negotiation, that coordinates on the machine learning algorithm to adapt to a shifting landscape. Also, this type of ML approach allows robots to compare catalogs or data sets, reinforce mutual observations, and correct omissions to react on the ground level based on the pre-installed and real-time data sets.