The DARPA Robotics Challenge (DRC) requires teams to integrate mobility, manipulation, and perception to accomplish several disaster-response tasks. We describe our hardware choices and software architecture, which enable human-in-the-loop control of a 28 degree-of-freedom ATLAS humanoid robot over a limited bandwidth link. We discuss our methods, results, and lessons learned for the DRC Trials tasks. The effectiveness of our system architecture was demonstrated as the WPI-CMU DRC Team scored 11 out of a possible 32 points, ranked seventh (out of 16) at the DRC Trials, and was selected as a finalist for the DRC Finals. C 2014 Wiley Periodicals, Inc.
Advances in technology in recent years have changed the learning behaviors of learners and reshaped teaching methods and learning environments. The purpose of this paper is to overview a foundational framework and provide models for the planning and implementation of smart learning environments. Introduction is focused on analysis of emerging industries and new types of jobs that are requiring future personnel to be well equipped to meet the need of the expansion requirements of these industries and keep up with their development needs. Gartner's 2015 Hype Cycle for Emerging Technologies identifies the computing innovations such as Internet of Things, Advanced Analytics, Machine Learning, Wearables etc. that organizations should monitor. Learners and students, being the future drivers of these industries, are the main human resource to fulfill the vacancies of these work forces. Constant improvements in and re-evaluation of the curriculum taught to the learners have to be done regularly to keep the learners up-to-date in fulfilling the requirements of these industries and corporations. Universities benefit from these thinking "out of the box practices" by equipping students with work force experience that involves more hands on task with real life infrastructures. Section 2 looks at future Internet domain landscape that comprises a great diversity of technology related topics involved in the implementation of Smart Learning Environments. The purpose of section 3 is to overview a foundational framework and major considerations for the planning and implementation of smart learning environments, behind which is the convergence of advances and developments in social constructivism, psychology, and technology. Section 4 introduces the smart learning models, which are developed to reflect the dynamic knowledge conversion processes in technology enabled smart learning environments. The last section presents a case study of a learning scenario entitled "Monitoring the environmental parameters in a Smart City" as an illustration of experimental learning on Internet of Things, which proofs the power of FORGE (forging online education through FIRE) FP7 project methodology and infrastructure for building remote labs and delivering them to students.
We present our preliminary results from the design process for developing the Worcester Polytechnic Institute's personal assistance robot, FRASIER, as an intelligent service robot for enabling active aging. The robot capabilities include vision-based object detection, tracking the user and help with carrying heavy items such as grocery bags or cafeteria trays. This work-in-progress report outlines our motivation and approach to developing the next generation of service robots for the elderly. Our main contribution in this paper is the development of a set of specifications based on the adopted user-centered design process, and realization of the prototype system designed to meet these specifications.
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