This paper presents a system in which a robot uses Active Learning (AL) to improve its learning capabilities for pose recognition. We propose a sub-type of Feature Queries, Rank Queries (RQ), in which the user states the relevance of a characteristic of the learning space. In the case of pose learning, these queries refer to the relevance of a single limb for a certain pose. We test the use of RQ with 24 users to learn 3 pointing poses and compare the learning accuracy against a passive learning approach. Our results show that RQ can increase the robot's learning accuracy.

In this paper we present an Open Source Community Oriented Project Based Learning (CO-PBL) educational model for engineering studies. We explain the pedagogical basis of our proposal and present a training course in which the model has been implemented. The chosen project has consisted of the design, building, and programming of a printable mobile robot (PrintBot). To print the robot's parts we used open source 3D printers, creating a community of students (Plastic Valley) interacting with other designers spread all over the world. As it is shown in this paper, this initiative has proven to be highly motivating to the students and it enabled an explosion of creativity.

An objective of natural Human-Robot Interaction (HRI) is to enable humans to communicate with robots in the same manner humans do between themselves. This includes the use of natural gestures to support and expand the information that is exchanged in the spoken language. To achieve that, robots need robust gesture recognition systems to detect the non-verbal information that is sent to them by the human gestures. Traditional gesture recognition systems highly depend on the light conditions and often require a training process before they can be used. We have integrated a low-cost commercial RGB-D (Red Green Blue - Depth) sensor in a social robot to allow it to recognise dynamic gestures by tracking a skeleton model of the subject and coding the temporal signature of the gestures in a FSM (Finite State Machine). The vision system is independent of low light conditions and does not require a training process.

Mobile ad-hoc network (MANET) technology is an essential piece in the path towards ubiquitous networking. In the past, most MANET research has been performed assuming the random waypoint mobility model for determining nodes movements. In the last years, some researchers have focused on alternative mobility models. In this paper, we perform a simulation based joint analysis of ad-hoc network performance at three layers (physical layer, network layer and transport layer) using four different mobility models, AODV as the routing protocol and TCP as the transport protocol. In some cases, we obtain counterintuitive results, which can be explained by correlating the behavior measured at the three considered layers