Carlos Agüero

Robotherapy with Dementia Patients

Humanoids have increasingly become the focus of attention in robotics research in recent years, especially in service and personal assistance robotics. This paper presents the application developed for humanoid robots in the therapy of dementia patients as a cognitive stimulation tool. The behaviour of the robot during the therapy sessions is visually programmed in a session script that allows music to play, physical movements (dancing, exercises, etc.), speech synthesis and interaction with the human monitor. The application includes the control software on board the robot and some tools like the visual script generator or a monitor to supervise the robot behaviour during the sessions. The robot applications impact on the patients health has been studied. Experiments with real patients have been performed in collaboration with a centre of research in neurodegenerative diseases. Initial results show a slight or mild improvement in neuropsychiatric symptoms over other traditional therapy methods.

Transparent Multi-Robot Communication Exchange for Executing Robot Behaviors

Service robots are quickly integrating into our society to help people, but how could robots help other robots? The main contribution of this work is a software module that allows a robot to transparently include behaviors that are performed by other robots into its own set of behaviors. The proposed solution addresses issues related to communication and opacity of behavior distribution among team members. This location transparency allows the execution of a behavior without knowing where is located. To apply our approach, a multi-robot distributed receptionist application was developed using robots that were not originally designed to cooperate among themselves.

Comparison of Smart Visual Attention Mechanisms for Humanoid Robots

Cameras are one of the most relevant sensors in autonomous robots. One challenge with them is to manage the small field of view of regular cameras. A method of coping with this, similar to the attention systems in humans, is to use mobile cameras to cover all the robot surroundings and to perceive all the objects of interest to the robot tasks even if they do not lie in the same snapshot. A gaze control algorithm is then required that continuously selects where the camera should look. This paper presents three different covert attention mechanisms that have been designed and compared: one based on round-Robin sharing, another based on dynamic salience and one with fixed pattern camera movements. Several experiments have been performed with a humanoid robot in order to validate them and to give an objective comparison in the context of RoboCup, where the robots have several perceptive needs like localization and object tracking that must be satisfied and may not be fully compatible.

Active Visual Perception for Humanoid Robots

Robots detect and keep track of relevant objects in their environment to accomplish some tasks. Many of them are equipped with mobile cameras as the main sensors, process the images and maintain an internal representation of the detected objects. We propose a novel active visual memory that moves the camera to detect objects in robots surroundings and tracks their positions. This visual memory is based on a combination of multi-modal filters that efficiently integrates partial information. The visual attention subsystem is distributed among the software components in charge of detecting relevant objects. We demonstrate the efficiency and robustness of this perception system in a real humanoid robot participating in the RoboCup SPL competition.

A Simple, Efficient, and Scalable Behavior-Based Architecture for Robotic Applications

In the robotics field, behavior-based architectures are software systems that define how complex robot behaviors are decomposed into single units, how they access sensors and motors, and the mechanisms for communication, monitoring, and setup. This paper describes the main ideas of a simple, efficient, and scalable software architecture for robotic applications. Using a convenient design of the basic building blocks and their interaction, developers can face complex applications without any limitations. This architecture has proven to be convenient for different applications like robot soccer and therapy for Alzheimer patients.

Multi-modal Active Visual Perception System for SPL Player Humanoid Robot

Robots detect and keep track of relevant objects in their environment to accomplish some tasks. Many of them are equipped with mobile cameras as the main sensors, process the images and maintain an internal representation of the detected objects. We propose a novel active visual memory that moves the camera to detect objects in robot’s surroundings and tracks their positions. This visual memory is based on a combination of multi-modal filters that efficiently integrates partial information. The visual attention subsystem is distributed among the software components in charge of detecting relevant objects. We demonstrate the efficiency and robustness of this perception system in a real humanoid robot participating in the RoboCup SPL competition.

Distributed perception for a group of legged robots

Perception problem in robotics has been usually faced from the individual perspective, in this work we present preliminary results of a shared perception mechanism for a group of legged robots working in a dynamic scenario. The experiments has been conducted using a group of aiBo robots in the RoboCup environment. Each individual robot has its own perception of a common interesting feature, the ball, and they altogether build a shared perception.