RPN (Robotic Programming Network) is an initiative to bring existing remote robot laboratories to a new dimension, by adding the flexibility and power of writing ROS code in an Internet browser and running it in the remote robot with a single click. The code is executed in the robot server at full speed, i.e. without any communication delay, and the output of the process is returned back. Built upon Robot Web Tools, RPN works out-of-the-box in any ROS-based robot or simulator. This paper presents the core functionality of RPN in the context of a web-enabled ROS system, its possibilities for remote education and training, and some experimentation with simulators and real robots in which we have integrated the tool in a Moodle environment, creating some programming courses and make it open to researchers and students (http: //robotprogramming.uji.es).

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The Robot Programming Network (RPN) is an initiative for creating a network of robotics education laboratories with remote programming capabilities. It consists of both online open course materials and online servers that are ready to execute and test the programs written by remote students. Online materials include introductory course modules on robot programming, mobile robotics and humanoids, aimed to learn from basic concepts in science, technology, engineering, and mathematics (STEM) to more advanced programming skills. The students have access to the online server hosts, where they submit and run their programming code on the fly. The hosts run a variety of robot simulation environments, and access to real robots can also be granted, upon successful achievement of the course modules. The learning materials provide step-by-step guidance for solving problems with increasing level of difficulty. Skill tests and challenges are given for checking the success, and online competitions are scheduled for additional motivation and fun. Use of standard robotics middleware (ROS) allows the system to be extended to a large number of robot platforms, and connected to other existing tele-laboratories for building a large social network for online teaching of robotics.

Many model based techniques have been proposed in the literature for applying domestic service tasks on humanoid robots, such as teleoperation, learning from demonstration and imitation. However sensor based robot control overcomes many of the difficulties of uncertain models and unknown environments which limit the domain of application of the previous methods. Furthermore, for service and manipulation tasks, it is more suitable to study the interaction between the robot and its environment at the contact point using the sensor based control, rather than specifying the joint positions and velocities required to achieve them.

When applying service robotic tasks using sensor based control, a classical exponential decrease of the error is usually used in the control laws which can reduces the performance of the executed task. In fact, due to this choice, the convergence time greatly increases especially at the end of the process. To ameliorate the performance of such tasks, we present in this paper two new error regulation strategies to accelerate the service tasks execution. These propositions are compared with the classical one in the case of performing autonomous object's manipulation tasks using real-time visual servoing. The Model Based Tracking method is used to apply head servoing and grasping of different objects using Nao humanoid robot.