Brett Browning

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Wepresent a comprehensive survey of robot Learning fromDemonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically,(More)
In an adversarial multi-robot task, such as playing robot soccer, decisions for team and single robot behavior must be made quickly to take advantage of short-term fortuitous events when they occur. When no such opportunities exist, the team must execute sequences of coordinated action across team members that increases the likelihood of future(More)
Coordinated action for a team of robots is a challenging problem, especially in dynamic, unpredictable environments. Robot soccer is an instance of a domain where well defined goals need to be achieved by multiple executors in an adversarial setting. Such domains offer challenging multiagent planning problems that need to coordinate multiagent execution in(More)
Coordinated action for a team of robots is a challenging problem, especially in dynamic, unpredictable environments. In the context of robot soccer, a complex domain with teams of robots in an adversarial setting, there is a great deal of uncertainty in the opponent’s behavior and capabilities. We introduce the concept of a play as a team plan, which(More)
As we progress towards a world where robots play an integral role in society, a critical problem that remains to be solved is the pickup team challenge; that is, dynamically formed heterogeneous robot teams executing coordinated tasks where little information is known a priori about the tasks, the robots, and the environments in which they would operate.(More)
Research in multi-agent systems has led to the development of many multi-agent control architectures. However, we believe that there is currently no known optimal structure for multi-agent control since the effectiveness of any particular architecture varies depending on the domain of the problem. Therefore, deployment of multi-agent teams would be(More)
Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher operates in two phases. The teacher first demonstrates the task(More)
Adversarial multi-robot problems, where teams of robots compete with one another, require the development of approaches that span all levels of control and integrate algorithms ranging from low-level robot motion control, through to planning, opponent modeling, and multiagent learning. Small-size robot soccer, a league within the RoboCup initiative, is a(More)
With the wide availability, high information content, and suitability for human environments of low-cost color cameras, machine vision is an appealing sensor for many robot platforms. For researchers interested in autonomous robot teams operating in highly dynamic environments performing complex tasks, such as robot soccer, fast color-based object(More)
In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out-of-core solution for loop-closing exactly sparse trajectories. Here we extend it to fuse exactly sparse trajectories, obtained from relative pose estimates, with absolute orientation data. This allows us to close-the-loop using absolute orientation data only.(More)