Carlos Antonio Acosta Calderon

Experimenting False Alarm Demand for Human Robot Interactions in Humanoid Soccer Robots

Performance of robots in human robot teams has always been a topic of interest for many researchers in human robot interaction community. Traditionally adopted neglect tolerance model for performance measurements assume ideal conditions in which the operator switches control between robots sequentially based on acceptable performance level for each robot ignoring any false alarms due to erroneous interactions. In this paper, we present the false alarm demand, a new metric for measuring effects of false alarms on human robot team performance and extend the neglect tolerance model to situations in which false positives and false negatives are prevalent. Experiments were performed with real and virtual humanoid soccer robots across tele-operated, and point to point modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the proposed extended neglect tolerance model predictions for real and virtual experiments for both autonomy modes. Experiments also showed that extended neglect tolerance model offered better estimation of robot performances as compared to the traditionally adopted neglect tolerance model for situations wherein false alarms are prevalent.

Imitation towards service robotics

We presented a new learning approach to the application of service robots, which is based on learning by imitation. Service robots need to increase their set of actions, which would lead to the ability of adapting their behaviours. In contrast with traditional learning approaches learning by imitation presents considerable advantages; equip robots with the abilities to be efficient in applications requiring human interaction. The paper offers our experiences with the first stage of our approach. Experimental results show the feasibility of such an approach.

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization. This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently. The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.

ON THE REDEFINITION OF FAN OUT METRIC FOR HUMAN ROBOT INTERACTIONS WITH HUMANOID SOCCER ROBOTS

Fan out (FO) is adopted as a general index among human robot interaction researchers in predicting the maximum number of robots a single operator can handle simultaneously while maintaining performance at acceptable levels. Neglect tolerance model forms the basis for FO metric that assumes ideal conditions wherein the operator switches control between robots sequentially based on acceptable performance ignoring any false alarms due to erroneous interactions. In this article, we redefine the FO metric to account for any additional demands due to the occurrence of false alarms, as these additional demands could lead to task failure. Experiments with our virtual and real humanoid soccer robots across tele-operation and semi-autonomous modes of autonomy showed significant drop in FO predictions with inclusion of demands due to false alarms for all experimental cases.

Recognition and generation of motion primitives with humanoid robots

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.

False alarm demand: A new metric for measuring robot performance in human robot teams

Performance of robots in human robot teams has always been a topic of interest for many researchers in human robot interaction community. Traditionally adopted Crandalls model for performance measurements assume ideal conditions in which the operator switches control between robots sequentially based on acceptable performance level ignoring any false alarms due to erroneous interactions. In this paper, we present the false alarm demand, a new metric for measuring effects of false alarms on human robot team performance and extend the Crandalls model to situations in which false positives and false negatives are prevalent. Experiments were performed with real and virtual humanoid soccer robots across tele-operated, and point to point modes of autonomy. Measured false alarms demand and robot performances were largely consistent with the proposed extended Crandalls model predictions for both real and virtual experiments for both autonomy modes.

Virtual-RE: A Humanoid Robotic Soccer Simulator

This paper focuses on the creation of a realistic simulation of a humanoid robot in a virtual environment. This model will help researchers to implement and study different behaviours of the humanoid robot Robo-Erectus without its physical presence. The dynamics and the appearance of the robot and the other objects within the simulator are faithfully reproduced in the virtual environment. Moreover, the virtual robot can be controlled with the same program controlling the real robot. The simulator has been used successfully to generate and test behaviours for the robot to prepare for the RoboCup 2008.

Development of an autonomous service robot for social interactions

Increasing research and development during these years has explored Human-Robot interactions especially for the challenge of the service robot. While service robots are becoming popular, these robots are just focusing into a specific tasks, and most of them are not ready prepare for the unpredictability of human environments. The efforts to develop more reliable robots that could interact with the human, are promoting the next generation of service robot. This type of robot would be equipped with perception, motion, and manipulation among other skills, to be help humans in daily tasks in different environments. This paper presents Snowie, a service robot used as Receptionist. The Snowie combines visual clues, voice, distance and PC input to detect humans and features in the environment. Guidance is one of the tasks that the receptionist robot must perform. To complete its task Snowie could receive extra information from smart beacons located in the building. The paper also explores two modalities to the guiding missions. Finally, experiments are presented in order to highlight the relevance of the approach.

Experimenting extended neglect tolerance model for human robot interactions in service missions

In this paper, we validate the extended neglect tolerance model for estimation of human robot team performance in relation to robot autonomy and compare its results with the traditionally adopted neglect tolerance model which assumes zero false alarms in human robot interactions. Extended neglect tolerance model estimates robot performance in human robot teams, where the human operator switches control between robots sequentially, based on acceptable performance levels, taking into account any false alarms and their respective demands. Experiments were performed with Robo-Erectus@Home, a service robot across tele-operation, and semi-autonomous modes of autonomy where a human operator controlled the robot to perform a walking assistant task. Measured false alarm demands and robot performances were largely consistent with the extended neglect tolerance model predictions for both autonomy modes. We also compared traditionally adopted neglect tolerance and extended neglect tolerance model for the same experimental design. The results showed that the latter offers better estimates of robot performance and attention demands, due to the inclusion of false alarms into the model.

Robotic companion lead the way

During last years, robotic research has explored Human-Robot interactions especially for the challenge of the robot companion. This type of robot would be equipped with perception, motion, and manipulation among other skills, to be help humans in daily tasks. Guidance is one of the tasks that the robot companion must perform. This paper describes Robbie, a wheeled robot companion uses for guiding missions. The Robbie combines visual clues, voice, distance and PC input to detect humans and features in the environment. To complete its task Robbie could receive extra information from smart beacons located in the building. The paper also explores two modalities to the guiding missions. Finally, experiments are presented in order to highlight the relevance of the approach.