2020/05/04 by Tyler Frasca, Evan Krause, Frasca, Tyler +5 · 1 citation
Computer Science · Engineering · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Reinforcement Learning in Robotics #Robotics (cs.RO) #Robotics and Automated Systems
paper · pdf · doi:10.48550/arxiv.2005.01544
openalex publication_date 2020/05/04 · openalex created_date 2020/05/13 · openalex updated_date 2026/07/28
Autonomous robots with sophisticated capabilities can make it difficult for human instructors to assess its capabilities and proficiencies. Therefore, it is important future robots have the ability to: introspect on their capabilities and assess their task performance. Introspection allows the robot to determine what it can accomplish and self-assessment allows the robot estimate the likelihood it will accomplish at given task. We introduce a general framework for introspection and self-assessment that enables robots to have task and performance-based dialogues before, during, and after a mission. We then realize aspects of the framework in the cognitive robotic DIARC architecture, and finally show a proof-of-concept demonstration on a Nao robot showing its self-assessment capabilities before, during, and after an instructed task.