100 AI MAGAZINE We envision the need for robots to be not only functional, but adaptable, robust to the diversity of human behaviors and speech patterns, and capable of acting in a both task and socially appropriate manner. Natural and diverse human-robot interaction (HRI) of this kind has been a long-standing goal for robotics research, and a broad range of approaches have been proposed for the development of robots that support diverse interactions. Among proposed techniques, variants that are dependent on hand-coded rule sets and probabilistic single-task policy learning methods have proven to be too brittle for interactive applications, failing to generalize over the diversity of possible inputs. Such systems typically force the user to adapt their method of interaction to fit the coded requirements of the robot. A different approach to creating more humanlike robotic systems has focused on imitating human cognitive processes by developing large scale cognitive architectures that support many modalities and interaction styles. While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. Common
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Crowdsourcing Real World Human-Robot Dialog and Teamwork through Online Multiplayer Games
Semantic Scholar · Computer Science · 2011
Abstract
100 AI MAGAZINE We envision the need for robots to be not only functional, but adaptable, robust to the diversity of human behaviors and speech patterns, and capable of acting in a both task and socially appropriate manner. Natural and diverse human-robot interaction (HRI) of this kind has been a long-standing goal for robotics research, and a broad range of approaches have been proposed for the development of robots that support diverse interactions. Among proposed techniques, variants that are dependent on hand-coded rule sets and probabilistic single-task policy learning methods have proven to be too brittle for interactive applications, failing to generalize over the diversity of possible inputs. Such systems typically force the user to adapt their method of interaction to fit the coded requirements of the robot. A different approach to creating more humanlike robotic systems has focused on imitating human cognitive processes by developing large scale cognitive architectures that support many modalities and interaction styles. While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. Common