Abstract :
In this paper we are mainly concentrating on the core concept and the benefits of an approach called Robo Earth which will be highly beneficial for future robotic applications in science and industry. Robo Earth is a world-wide platform which robots can use to exchange position and map information as well as task-related, hardware independent action recipes. This will enable manufacturers worldwide to break down their costs and efforts for reproducing software algorithms for robot behavior over and over again. The Robo Earth framework can store all relevant data from algorithms to complex behavior descriptions that allows robots to act autonomously in an unknown, unspecified environment. Especially in the field of interaction with humans. Robo Earth can bring forward the behavior of robots and simplify the software design for developers in that field.
I. INTRODUCTION
Todays robots are not capable of understanding unstructured environments. The systems available rely on the specification of every eventuality a system will have to cope with in executing its tasks. Each response to a contingency has to be programmed in advance. During performing such a pre-programmed task, present robots almost completely rely on feedback. Looking at the result of its action, the robot will try to make the necessary adjustments. The worst thing about this is that the next time the robot has to perform the same action in the same environment again, it has to start all over again, build a world model from sensor data and close the feedback loop to adjust the actions needed to accomplish the task at hand. The lack of memorization and learning prevents the robot from improving its sensing and action capabilities over time. This approach faces serious limitations because the real world is generally too nuanced, too complicated and too unpredictable to be summarized within a limited set of specifications. There will inevitably be novel situations and the robotic system will always have gaps, conflicts or ambiguities in its knowledge and action instructions. Furthermore the growing number of cheap sensors, especially networked sensors and increased resolution of sensors result in an exponential growth in sensor data. This raises the problem of extracting meaning and purpose from these bursts of sensor data. A solution to the above problems is to store the robots knowledge of the environment and the actions needed to perform its task in a global world-wide accessible database. If that particular robot or any other robot is to perform a similar action in a more or less similar environment and it has access to the stored knowledge, the robot can even if it did not perform the task before- improve on the earlier obtained sensing and action result. This is exactly what the Robo Earth approach is about: sharing knowledge between robots all over the world by building up a huge knowledge database on the world with its objects, their affordances and high level action description data (called recipes), that describe actions of the robots in general way applicable to different hardware platforms that are not completely identical in construction. This reusable knowledge of the world will provide a powerful feed forward to any robots 3D sensing, acting and learning capabilities.
More >> ROBO EARTH
Todays robots are not capable of understanding unstructured environments. The systems available rely on the specification of every eventuality a system will have to cope with in executing its tasks. Each response to a contingency has to be programmed in advance. During performing such a pre-programmed task, present robots almost completely rely on feedback. Looking at the result of its action, the robot will try to make the necessary adjustments. The worst thing about this is that the next time the robot has to perform the same action in the same environment again, it has to start all over again, build a world model from sensor data and close the feedback loop to adjust the actions needed to accomplish the task at hand. The lack of memorization and learning prevents the robot from improving its sensing and action capabilities over time. This approach faces serious limitations because the real world is generally too nuanced, too complicated and too unpredictable to be summarized within a limited set of specifications. There will inevitably be novel situations and the robotic system will always have gaps, conflicts or ambiguities in its knowledge and action instructions. Furthermore the growing number of cheap sensors, especially networked sensors and increased resolution of sensors result in an exponential growth in sensor data. This raises the problem of extracting meaning and purpose from these bursts of sensor data. A solution to the above problems is to store the robots knowledge of the environment and the actions needed to perform its task in a global world-wide accessible database. If that particular robot or any other robot is to perform a similar action in a more or less similar environment and it has access to the stored knowledge, the robot can even if it did not perform the task before- improve on the earlier obtained sensing and action result. This is exactly what the Robo Earth approach is about: sharing knowledge between robots all over the world by building up a huge knowledge database on the world with its objects, their affordances and high level action description data (called recipes), that describe actions of the robots in general way applicable to different hardware platforms that are not completely identical in construction. This reusable knowledge of the world will provide a powerful feed forward to any robots 3D sensing, acting and learning capabilities.
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