Reinforcement Learning Algorithms in Humanoid Robotics

Abstract

Many aspects of modern life involve the use of intelligent machines capable of operating under dynamic interaction with their environment. In view of this, the field of biped locomotion is of special interest when human-like robots are concerned. Humanoid robots as anthropomorphic walking machines have been in operation for more than twenty years. Currently, research on the design and the humanoid robots are one of the most exciting and challenging topics in the field of robotics. .The potential applications of this research area are very foremost in the middle and long term. Humanoid robots are expected to be servants and maintenance machines with the main task to assist human activities in our daily life and to replace humans in hazardous operations. It is as obvious as interesting that anthropomorphic biped robots are potentially capable to effectively move in all unstructured environments where humans do. There also raises strong anticipations that robots for the personal use will coexist with humans and provide supports such as the assistance for the housework, care of the aged and the physically handicapped. Consequently, humanoid robots have been treated as subjects of robotics researches such as a research tool for human science, an entertainment/mental-commit robot or an assistant/agent for humans in the human living environment. Humanoid robot are autonomous systems capable of extracting information from their environments and using knowledge about the world and intelligence of their duties and proper governing capabilities. Intelligent humanoid robots should be autonomous to move safely in a meaningful and purposive manner, i.e. to accept high-level descriptions of tasks (specifying what the user wants to be done, rather than how to do it) and would execute them without further human intervention. Future humanoid robots are likely to have greater sensory capabilities, more intelligence for valid reasoning and decision making, higher levels of manual dexterity and adequate mobility as compared to humans. Naturally, the first approach to making humanoid robots more intelligent was the integration of sophisticated sensor systems as computer vision, tactile sensing, ultrasonic and sonar sensors, laser scanners and other smart sensors. However, today’s sensor products are still very limited in interactivity and adaptability to changing environments. As the technology and algorithms for real-time 3D vision and tactile sensing improve, humanoid robots will be able to perform tasks that involve complex interaction with the environment (e.g. grasping and manipulating the objects). A major reason is that uncertainty and dynamic changes make the development of reliable artificial systems particularly challenging. On the other