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Knowledge Eng. Review. 01/2011; 26:11-17.
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Trends in Intelligent Robotics - 13th FIRA Robot World Congress, FIRA 2010, Bangalore, India, September 15-17, 2010. Proceedings; 01/2010
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ABSTRACT: While the robots that most quickly come to mind to the general public are those with the most elaborate features and movements,
those that are most useful in advancing the state of the art in artificial intelligence (AI) are very different. Minimalist
robots are inexpensive and therefore more broadly available for research and educational purposes, but also force the researcher
to rely on good, adaptable solutions to hard AI problems rather than relying on expensive specialized hardware that will only
work under strict conditions. This chapter describes our work in minimalist humanoid robots, focussing mainly on Tao-Pie-Pie,
a robot that competed successfully in numerous RoboCup and FIRA competitions. The chapter describes our motivations in designing
minimalist robots and our rationale for working with humanoid robots, and describes the development of Tao-Pie-Pie, including
contrasting this robot with other work and developing its walking gait and balancing reflexes. We then describe some issues
in evaluating humanoid robots, and describe ongoing work.
01/2009: pages 355-376;
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Experimental Design for Real-World Systems, Papers from the 2009 AAAI Spring Symposium, Technical Report SS-09-03, Stanford, California, USA, March 23-25, 2009; 01/2009
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RoboCup 2009: Robot Soccer World Cup XIII [papers from the 13th annual RoboCup International Symposium, Graz, Austria, June 29 - July 5, 2009]; 01/2009
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RoboCup 2008: Robot Soccer World Cup XII [papers from the 12th annual RoboCup International Symposium, Suzhou, China, July 15-18, 2008]; 01/2008
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Using AI to Motivate Greater Participation in Computer Science, Papers from the 2008 AAAI Spring Symposium, Technical Report SS-08-08, Stanford, California, USA, March 26-28, 2008; 01/2008
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RoboCup 2008: Robot Soccer World Cup XII [papers from the 12th annual RoboCup International Symposium, Suzhou, China, July 15-18, 2008]; 01/2008
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ABSTRACT: This chapter has reviewed some of the issues involved in creating pragmatic global vision systems. We have discussed the assumptions on which traditional systems are based, pointed out how these differ with the observed abilities of human vision, and described how these assumptions limit the applicability and generality of existing systems. We then described techniques that allow some of these assumptions to be discarded, and the embodiment of these techniques in our production global vision systems, Doraemon and Ergo. Both Doraemon and Ergo are used in a number of ways. Doraemon has been in use every year by a number of teams from around the world in the F-180 (small-size) league at RoboCup. Ergo is the current global vision system in use in our own laboratories, and is currently being employed in a number of projects, such as imitation learning in groups of robots (Allen, 2007). We have also described some of our recent work toward creating much more general global vision systems that take advantage of additional knowledge or adaptability in order to avoid the need for any type of predefined markings on objects. The latter work is very preliminary, but shows the potential for improved techniques to eventually be the basis for more general vision systems. In working toward such generality today, there are a number of very important areas of immediate future work. Existing approaches to global vision are well-understood and immediately deployable. The fact that they rely heavily on elements such as the ability to recognize colour patches, for example, means that anything that can be done to improve these abilities will serve to improve existing systems. While systems such as Doraemon are already exploiting much in terms of maximizing flexibility while still assuming colours can be defined and matched, future work may still improve this further. Any small steps that can be performed to wean existing systems away from their traditional assumptions will serve as a backbone for further future work. While Ergo is a significant improvement over the abilities of Doraemon, for example, it still conforms to some traditional assumptions in terms of relying on predefined patterns, and instead exploits different mechanisms to be more flexible and offer a better performance in a wider array of domains. There will be many similar steps as we move to more general vision systems. Any single tracking or identification technique has its limitations, and just as neither Ergo nor Doraemon use a single mechanism to identify and track objects, future systems will require a synergy of techniques. Attempting to leverage the strengths of techniques off of one another will always be an important part of future work in this area. In our own work, we are currently attempting to employ the addition of control knowledge to the subsymbolic orientation recognition described in Section 4.2. For example, if we are uncertain of a robot's location and orientation at the current time, we can start with the robot's last known location/orientation at previous time, and constrain the potential solution set by the likely outcome of the most recent command sent to the robot. The iterative steps taken in improving global vision are in turn a useful source of future work in improving application areas as well. For example, the work on recognizing orientation without markers described in Section 4.2 was undertaken as convenient subproblem of the overall vision task useful in robotic soccer, in order to track a team’s own players for control purposes. The ability to infer robots' orientation without prior
02/2007; , ISBN: 3-86611-283-1
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Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada; 01/2007
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Robots and Robot Venues: Resources for AI Education, Papers from the 2007 AAAI Spring Symposium, Technical Report SS-07-09, Stanford, California, USA, March 26-28, 2007; 01/2007
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Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA; 01/2006
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RoboCup 2005: Robot Soccer World Cup IX; 01/2005
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ABSTRACT: Robotic soccer is a highly complex domain that has become a significant challenge problem in both mobile robotics and artificial intelligence. Two well-known annual competitions, ROBOCUPand FIRA, allow teams to compete in a number of different leagues distinguished by robot size and hardware restrictions. Because the domain is based on teamwork in a real-time environment, we have found agent-based control of individual robots to be a very convenient approach to designing a robotic soccer team. While we have worked with both local vision and global vision robots in the past, our most recent work has been with global vision. This paper describes the nature of this robotic domain, its suitability to agent-based methodologies, some of our particular motivations, and our use of agent-based control to deal with the difficult problems it encompasses.
06/2004;
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ABSTRACT: The ability to form coalitions of agents is central to multiagent problem-solving. However, most multi-agent systems research still takes the view that teams are simply provided - an invalid assumption in most real-world situations. This paper describes an approach to forming coalitions of agents in robotic soccer, a domain where the dynamic nature of the environment plays a key role. We describe how agents that can learn about the abilities of others can form a coalition of the better-playing agents on the team, and show that this can be used to improve the performance of a team consisting of agents with varying skill levels. We also show that this mechanism is a useful one in a setting where agents are learning to play soccer, in order to form a coalition of agents from which to learn.
06/2004;
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RoboCup 2004: Robot Soccer World Cup VIII; 01/2004
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RoboCup 2004: Robot Soccer World Cup VIII; 01/2004
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ABSTRACT: This paper describes methods used in stabilizing the walking gait of Tao-Pie-Pie, a small humanoid robot given rate feedback from two RC gyroscopes. Tao-Pie-Pie is a fully autonomous small humanoid robot (30cm tall). Although Tao-Pie-Pie uses a minimal set of actuators and sensors, it has proven itself in international competitions, winning honors at the RoboCup and FIRA HuroSot competitions in 2002 and 2003. The feedback control law is based solely on the rate information from two RC gyro-scopes. This alleviates drift problems introduced by integrating the RC gyroscope feedback in the more common position control approaches.
09/2003;
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AAAI Mobile Robot Competition 2003, Papers from the AAAI Workshop; 01/2003
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ABSTRACT: Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's stand-points. 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 illus-trated the potential of this approach both in providing scaf-folding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this pa-per, 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 con-cepts, 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 stu-dent motivation, and also provides some very useful software engineering experience to the students.
