The field of robotics, and more especially humanoid robotics, has several established competitions with research oriented goals in mind. Challenging the robots in a handful of tasks, these competitions provide a way to gauge the state of the art in robotic design, as well as an indicator for how far we are from reaching human performance. The most notable competitions are RoboCup, which has the long-term goal of competing against a real human team in 2050, and the FIRA HuroCup league, in which humanoid robots have to perform tasks based on actual Olympic events. Having robots compete against humans under the same rules is a challenging goal, and, we believe that it is in the sport of archery that humanoid robots have the most potential to achieve it in the near future. In this work, we perform a first step in this direction. We present a humanoid robot that is capable of gripping, drawing and shooting a recurve bow at a target 10 meters away with considerable accuracy. Additionally, we show that it is also capable of shooting distances of over 50 meters.

Magicians have been a source of entertainment formany centuries, with the ability to play on human bias,and perception to create an entertaining experience.There has been rapid growth in robotics throughoutindustrial applications; where primary challenges in-clude improving human-robot interaction, and roboticperception. Despite preliminary work in expressive AI,which aims to use AI for entertainment; there has notbeen direct application of fully embodied autonomousagents (vision, speech, learning, planning) to enter-tainment domains. This paper describes preliminarywork towards the use of magic tricks as a methodfor developing fully-embodied autonomous agents. Acard trick is developed requiring vision, communica-tion, interaction, and learning capabilities all of whichare coordinated using our script representation. Ourwork is evaluated quantitatively through experimen-tation, and qualitatively through acquiring 2nd placeat the 2016 IROS Humanoid Application Challenge.A video of the live performance can be found at https://youtu.be/OMpcmcPWAVM.

Formations are often seen in nature, and bring many benefits for the group as a whole. They can allow a group to explore a large area more effectively, can ease movement of the group through the environment, and can increase group perceptual coverage and increase defensive capabilities, for example. The benefits of any particular formation vary and are obtained from the structure the formation provides. Robotic formations can have similar applications. To date, the techniques used and formations employed in robotic applications are significantly simpler than those seen in nature. Current techniques often require some level of global knowledge, central processing or other unrealistic assumptions. We seek to develop a formation control technique that has as few of these limitations as possible. Each agent under our approach has only local knowledge of the environment, uses no broadcast communication, and can communicate only over a limited range. Formations are achieved by organizing agents into a graph structure, where agents occupying the vertices take on the role of maintaining an appropriate number of agents on each edge, thus preserving the formation's shape and scale. We do not assume a known or static population: the evolving formation acts as a physical data structure to assist in placing and rearranging agents as the population changes. This approach does not require a global coordinate system, fixed positions within the formation, or any single lead agent. All agents within our approach are peers, and any can adopt any role within the formation.

Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.

Until recent years, the development of real-world humanoid robotics applications has been hampered by a lack of available mobile computational power. Unlike wheeled platforms, which can reasonably easily be expected to carry a payload of computers and batteries, humanoid robots couple a need for complex control over many degrees of freedom with a form where any significant payload complicates the balancing and control problem itself. In the last few years, however, an significant number of options for embedded processing suitable for humanoid robots have appeared (e.g. miniaturized motherboards such as beagle boards), along with ever-smaller and more powerful battery technology. Part of the drive for these embedded hardware breakthroughs has been the increasing demand by consumers for more sophisticated mobile phone applications, and these modern devices now supply much in the way of sensor technology that is also potentially of use to roboticists (e.g. accelerometers, cameras, GPS). In this paper, we explore the use of modern mobile phones as a vehicle for the sophisticated AI necessary for autonomous humanoid robots.

Using robots as part of any curriculum requires careful management of the significant complexity that physical embodiment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigating students through this complexity is the biggest challenge faced by an instructor.  Achieving this requires a framework that allows complexity to be introduced in stages, as students' abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student  sophistication, and be able to quickly change between applications.  It should be portable and maintainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeatability and control for evaluating the students' work, as well as for performing research. In this paper, we discuss the advantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges.  We introduce a framework for managing mixed reality in the classroom, and discuss our experiences with using this framework for teaching robotics and AI.