This paper describes the Player/Stage software tools applied to multi-robot, distributed-robot and sensor network systems. Player is a robot device server that provides network transparent robot control. Player seeks to constrain controller design as little as possible; it is device independent, non-locking and language- and style-neutral. Stage is a lightweight, highly configurable robot simulator that supports large populations. Player/Stage is a community Free Software project. Current usage of Player and Stage is reviewed, and some interesting research opportunities opened up by this infrastructure are identified.

1 Introduction and Motivation Multi-robot task allocation (MRTA) algorithms for heterogeneous groups ofrobots have to be able to differentiate between robots based on their performance in order to optimize allocation. Existing MRTA algorithms [?,?] gener-ally do this based on hand-coded information about the task utilities relative to each robot. Using hand-coded task utilities, however, these algorithms aretypically not sensitive to the effects of group dynamics, such as interference and synergy. These effects typically have to be estimated at runtime as theyare difficult to model due to their volatility and complexity.

Existing multi-robot task allocation algorithms (MRTA) generally do not consider the e#ects of interaction, such as interference, but instead assume that tasks are independent. That assumption, however, is often violated in groups of cooperative mobile robots, where interaction e#ects can have a critical impact on performance. Modeling the e#ects of interaction, or group dynamics, is problematic due to their complexity and volatility, which also makes it di#cult to hand-code optimal solutions to MRTA problems. We present a distributed MRTA algorithm that is sensitive to interaction dynamics. The algorithm uses distributed reinforcement learning to make interference-sensitive estimates of task utilities and relies on stigmergy to let optimal allocations emerge. The algorithm is inspired by vacancy chains, a resource distribution process common in human and animal societies. We present a formal model of task allocation through vacancy chains that defines optimal allocations in MRTA problems in terms of interference-sensitive measures of individual robot contributions. We validate our model by demonstrating, in simulation, how the predicted allocations are produced by our algorithm. As the robots continuously update their individual utility estimates, the vacancy chain algorithm has the additional property of adapting automatically to changes in the environment, e.g., robot breakdowns or changes in task values. We study the problem of cooperative transportation and demonstrate that our algorithm improves on the performance of hand-coded solutions. Finally, as the vacancy chain algorithm uses no communication or unique roles and is more robust and scalable than alternative algorithms with significant communication overheads.