In this paper, we present a commercial sensor node platform called ScoutNode. It is based on a novel modular architecture that supports three very powerful functionalities - multimodal support with a maximum of twenty five sensors supported on a single node that includes multiple video cameras; a user- friendly API for application development, and interoperability with a variety of 802.x hardware and protocols. ScoutNode architecture has been developed to support the operational concept and requirements of unattended ground sensor (UGS) nodes for net-centric information collection for the military. It offers a very comprehensive sensing, processing, and communication platform for building a variety of military and commercial applications. The ScoutNode platform described in this paper is fully functional and a node based on this platform designed specially for the military was tested successfully by the US Army.

Effective human/robot interfaces which mimic how humans interact with one another could ultimately lead to robots being accepted in a wider domain of applications. We present a framework for interactive task training of a mobile robot where the robot learns how to do various tasks while observing a human. In addition to observation, the robot listens to the human's speech and interprets the speech as behaviors that are required to be executed. This is especially important where individual steps of a given task may have contingencies that have to be dealt with depending on the situation. Finally, the context of the location where the task takes place and the people present factor heavily into the robot's interpretation of how to execute the task. In this paper, we describe the task training framework, describe how environmental context and communicative dialog with the human help the robot learn the task, and illustrate the utility of this approach with several experimental case studies.

In order for robots to act as valuable assistants for non-expert users, they need to be able to learn new abilities and do so through natural methods of communication. Furthermore, it is often desirable that tasks be learned quickly without having to provide multiple demonstrations. Training should also be conducted in such a way that the user has a clear understanding of the manner in which environmental features affect the behavior of the learned activity, so that execution behavior is predictable. We present an interactive framework for teaching a robot the flow of an activity composed of elements from a set of primitive behaviors and previously trained activities. Conditional branching and looping, order-independent activity execution, and contingency (or interrupt) actions can all be captured by our activity structures. Additional convenience functionality to aid in the training process is also provided. By providing a natural method of communicating production rules analogous to rigid programming structures, well-defined tasks can be trained easily. We demonstrate our task training procedure on a mobile robot.