One of the key elements for building a long-term robotic companion is incorporating the ability for a robot to continuously learn and engage in new tasks. Utilizing a defined workspace that provides various shared content between human and robot could assist in this learning process. Here, we propose integrating a touchscreen tablet and a robot learner for engaging the user during human-robot interaction scenarios. The robot learner's domain-independent core reasoner follows the structure of instance-based learning which addresses the issues of acquiring knowledge, encoding cases, and learning a retrieval metric. The system utilizes demonstrations provided by the user to auto-populate the knowledge base through natural interaction methods, encodes cases based on the feature structure provided by the user, and uses an adaptive-weighting technique to design a retrieval metric with linear regression in the feature-distance space. Through a tablet environment, the user teaches a task to the robot in a shared workspace and intuitively monitors the robot's behavior and progress in real time. In this setting, the user is able to interrupt the robot and provide necessary demonstrations at the moment learning is taking place, thus providing a means to continuously engage both the participant and the robot in the learning cycle.

Icebergs generated from ice ablation processes continue to be a threat for operations conducted in polar regions. Systems that have been developed to track and observe these threats often use either space-based radar imaging or visual observation by the crew of the ship. Both of these methods have disadvantages, mostly in terms of real-time observation or the physical abilities of the crew. We propose a robotic solution for in-situ observation of icebergs, so that countermeasures may be quickly implemented. Our focus in this work is the problem of allocating resources to observation regions: once areas of iceberg activity have been identified, how are robot observers assigned to these regions and what cost metric may be used to determine the best placement of robot observers. Our solution is currently demonstrated and evaluated in simulation.

Environmental monitoring of spatially-distributed geo-physical processes (e.g., temperature, pressure, or humidity) requires efficient sampling schemes, particularly, when employing an autonomous mobile agent to execute the sampling task. Many approaches have considered optimal sampling strategies which specialize in minimizing estimation error, while others emphasize reducing resource usage, yet rarely are both of these performance parameters used concurrently to influence the navigation. This work discusses how a spatial estimation process and resource awareness are integrated to generate an informed navigation policy for collecting useful measurement information. We also enable a direct comparison between this informed navigation method and more common approaches using two performance metrics. We show that our informed navigation outperforms these approaches based on performance evaluation as a function of estimation error and resource usage for a useful range of coverage within the sampling area.

Ships that operate in polar regions continue to face the threat of floating ice sheets and icebergs generated from an ice ablation process. Systems have been implemented to track these threats, with varying degrees of success. In this paper, a definition is proposed for this tracking problem that re-casts it within a class of robotic, multiagent target observation problems. The focus in this new definition is on minimizing the time an initial contact for newly generated targets is obtained, as opposed to obtaining a contact long after a target has been generated. Focusing on the initial contact time provides for the ability to enact early countermeasures. A model is provided for the target sources, as well as metrics for computing costs associated with the model for reallocating robotic agents during an observation task. The effectiveness of the solution compared with an existing observation technique is demonstrated using simulation.