BWIBots: A platform for bridging the gap between AI and human–robot interaction research

Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Artificial Vision in the Nao Humanoid Robot

Projecte Final de Màster UPC realitzat en col.laboració amb l'Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i MatemàtiquesRobocup is an international robotic soccer competition held yearly to promote innovative research and application in robotic intelligence. Nao humanoid robot is the new RoboCup Standard Platform robot. This platform is the new Nao robot designed and manufactured by the french company Aldebaran Robotics. The new robot is an advanced platform for developing new computer vision and robotics methods. This Master Thesis is oriented to the study of some fundamental issues for the artificial vision in the Nao humanoid robots. In particular, color representation models, real-time segmentation techniques, object detection and visual sonar approaches are the computer vision techniques applied to Nao robot in this Master Thesis. Also, Nao’s camera model, mathematical robot kinematic and stereo-vision techniques are studied and developed. This thesis also studies the integration between kinematic model and robot perception model to perform RoboCup soccer games and RoboCup technical challenges. This work is focused in the RoboCup environment but all computer vision and robotics algorithms can be easily extended to another robotics fields

Transfer Learning using Computational Intelligence: A Survey

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..