Adaptive human-robot teaming through integrated symbolic and subsymbolic artificial intelligence: preliminary results

摘要

As the autonomy of intelligent systems continues to increase, the ability of humans to maintain control over machine behavior, work effectively in concert with them, and trust them, becomes paramount. Ideally, a machine’s plan of action would be accessible to and understandable by human team members, and machine behavior would be modifiable in real time, in the field, to accommodate unanticipated situations. The ability of machines to adapt to new situations quickly and reliably based on both human input and autonomous learning has the potential to enhance numerous human-machine teaming scenarios. Our research focuses on the question, “Can robots become competent and adaptive teammates by emulating human skill acquisition strategies?” In this paper we describe the Robotic Skill Acquisition (RSA) cognitive architecture and show preliminary results of teaming experiments involving a human wearing an augmented reality headset and a quadruped robot performing tasks related to reconnaissance. The goal is to combine instruction and discovery by integrating declarative symbolic AI and reflexive neural network learning to produce robust, explainable and trusted robot behavior, adjustable autonomy, and adaptive human-robot teaming. Humans and robots start with a playbook of modifiable hierarchical task descriptions that encode explicit task knowledge. Neural network based feedback error learning enables human-directed behavior shaping, and reinforcement learning enables discovery of novel subtask control strategies. It is anticipated that modifications to and transitions between symbolic and subsymbolic processing will enable highly adaptive behavior in support of enhanced situational awareness and operational effectiveness of human-robot teams.