Application Of Machine Learning and Robotics

    8 Votes

Machine Learning is a branch of Artificial Intelligence and is gaining importance. If Machine Learning is applied to robots then they can learn by themselves and thus help to reduce the tedious task of monitoring, programming and also will minimize the overhead charges. The paper takes into consideration the introduction of Machine Learning and its applications to robotics with a case study. The paper briefly explains the method of learning with a case study of system able of performing complex manipulation task. A manipulation system that can observe dynamic changes in the environment in real time. This consists of a grasping system that uses a high-speed visual and force feedback technique having a multi-fingered hand-arm with a hierarchical parallel processing system and a high-speed vision system SPE-256 and Grasping with high responsiveness and adaptiveness to dynamic changes in the environment is realized by Machine Learning.

Machine Learning is a new and rapidly developing area of technology with a great potential in a wide variety of applications. It deals with machines performing human like functions such as reasoning and interpretation. Machine Learning is a term that is usually applied to functions performed by machines that would normally require some properties of human intelligence. This typically involves either human like reasoning or senses. It is not just a software or hardware technology. It involves an entire process of data collection, computer logic and data processing .Development efforts in this area require advances not only in the fields of computer science but also in the basic understanding of the human senses and the brain. These machines do not operate like conventionally computer systems, which follow the instruction step by step. They use association, reasoning and decision making processes much like human brain would to solve problems. Some can learn from experience and communicate in natural language just as children grow from infancy to adulthood.

This new generation systems differ from contemporary systems primarily in the ability to draw conclusions and make decisions based on the inference and deduction from base of knowledge. Because of their ability to handle data with some level of meaning and understanding, such machines can be used as intelligent problem solvers rather than merely as computation devices that follow strict instructions. Machine Learning can simulate many human capabilities, but it yet cannot create. This requires imagination and intuition, not just logic. Thus Machine learning is decision making ability by means of artificial intelligence to exhibit intelligent behaviour without any help of a human being which involves Machines that think like humans and act like humans.


1) Intelligent systems can learn & act accordingly. 
2)  It can be found that providing good examples and a good interface between the learning and the performance components is crucial for success.
3) In short, besides the technological challenges of mobile robotics fundamental sensor-motor competences, robot navigation and application-oriented capabilities the scientific challenge is to move mobile robotics from a discipline of empirical practice towards a precise science.

Download this file (Application Of Machine Learning and Robotics.doc)Application Of Machine Learning and Robotics[Seminar Report]245 Kb

Popular Videos


How to improve your Interview, Salary Negotiation, Communication & Presentation Skills.

Got a tip or Question?
Let us know

Related Articles

Conceptual design Of Hybrid Scooter Transmission With Planetory Gear-Train
Vibratory Stress Relief In Manufacturing Processes
Stress Analysis And Optimization Of Weld Penetration Problem In Butt Welded Joints
Biodiesel - Tomorrows Fuel
Optical Fiber Sensors In Medicine
Hybrid Fuel Cell Electric Vehicles
Agile Manufacturing – A Recent Trend In Manufacturing
IC Engines with Homogeneous Combustion In Porous Medium
Finite Element Analysis Of Automobile Suspension System
New Trends In Manufacturing - Rapid Prototyping
Technical Paper On Six Stroke Engine
Micro Machined Microsensors for the measurement of Mechanical Signals
TQM - A Successful Journey
Rapid Prototyping of Robotic Systems
A Control Classification of Automated Guided Vehicle Systems
Total Quality Management In Business Process Reengineering
Homogeneous Charge Compression Ignition - Future Of IC Engines
Vehicle Operator Safety - The Advantages of Using Electronic Sensors in Off-road Vehicles
Open Architecture Made Easy With CIM
Rapid Prototyping