Using a shared tablet workspace for interactive demonstrations during human-robot learning scenarios

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.