The appropriate management of fall situations - i.e. fast instability detection, avoidance of unintentional falls, falling without damaging the body, fast recovering of the standing position after a fall - is an essential ability of biped humanoid robots. This issue is especially important for humanoid robots carrying out demanding movements such as walking in irregular surfaces, running or practicing a given sport (e.g. soccer). In a former contribution we have addressed the design of low-damage fall sequences, which can be activated/triggered by the robot in case of a detected unintentional fall or an intentional fall (common situation in robot soccer). In this article we tackle the detection of instability and the avoidance of falls in biped humanoids, as well as the integration of all components in a single framework. In this framework a fall can be avoided or a falling sequence can be triggered depending on the detected instability's degree. The proposed fall detection and fall avoidance subsystems are validated in real world-experiments with biped humanoid robots.

In this article, a virtual environment for realistic testing and training of face detection and recognition systems under uncontrolled conditions is proposed. The key elements of this tool are a simulator and real face and background images taken under real-world conditions with different acquisition conditions. Inside the simulated environment, an observing agent, the one with the ability to recognize faces, can navigate and observe the real face images, at different distances, angles and with indoor or outdoor illumination. During the face recognition process, the agent can actively change its viewpoint and relative distance to the faces in order to improve the recognition results. The virtual environment provides all behaviors to the agent (navigation, positioning, face's image composing under different angles, etc.), except the ones related with the recognition of faces. This tool could be of high interest in HRI applications related with the visual recognition of humans. It allows comparing and quantifying the face recognition capabilities of service robots, and in general intelligent machines, under exactly equal working conditions. The applicability of the proposed tool is validated in the comparison of state of the art face detection and recognition methods.