Much research effort in HRI has focused on how to enable robots to learn new skills from observations, demonstrations, and instructions. Less work, however, has focused on how skills can be corrected if they were learned incorrectly, adapted to changing circumstances, or generalized/specialized to different contexts. In this paper, a skill modification framework is introduced that allows users to modify a robot’s stored skills quickly through instructions to (1) reduce inefficiencies, (2) fix errors, and (3) enable generalizations, all in a way for modified skills to be immediately available for task performance. A thorough evaluation of the implemented framework shows the operation of the algorithms integrated in a cognitive robotic architecture on different fully autonomous robots in various HRI case studies. An additional online HRI user study verifies that subjects prefer to quickly modify robot knowledge in the way we proposed in the framework.

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt!" challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.

Learning new knowledge from single instructions and being able to apply it immediately is highly desirable for artificial agents. We provide the first demonstration of spoken instruction-based one-shot object and action learning in a cognitive robotic architecture and briefly discuss the architectural modifications required to enable such fast learning, demonstrating the new capabilities on a fully autonomous robot.

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods ofHCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efficiently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. We present results from offline analysis, as well as end-to-end operation using a robotic arm.