of the Pengo9000 (the members of Group B), to create a coworker robot that will make it possible for him to triple his profits without adding manpower or major retooling costs.

The group’s moderator, Matthias Scheutz, summarized the group’s discussion. Scheutz initiated his discussion by commenting that the group found the exercise immensely challenging—so much so that solving this scenario required solving all of AI. Thus the group decided to separate the “spirit of the exercise” from a prototype that could potentially be available in a two-year time frame. Aspirationally, a collaborative robot would have natural language capabilities and would be able to learn and generalize from its lessons to real-world task completion. The robot would have sensing and perception capabilities that would, for example, enable it to distinguish between different kinds of wood, drill-bit requirements, and so on. It would have “common sense” knowledge, in addition to the domain knowledge necessary for understanding the commonsense meaning of words. For example, when someone is told to “go to the kitchen and turn the stove on,” a human understands that he must go to the kitchen before he turns the stove on. A conventional robot might be expected to know that the word “and” refers to parallel, sequential, or temporal sequencing, but it would not have the intuitive capability to infer the correct meaning. The aspirational collaborative robot would be able to take directions from a combination of verbal and gestural cues. Finally, that robot would have perceptual and actuation capabilities that would enable it to find chairs in another room and then know which ones need to be drilled.

The group suggested that a robot could be developed within two years to fulfill certain tasks. It would have effectors for drilling and clamping and algorithms for planning and scheduling, as well as detecting and targeting objects. The robot would also understand simple instructions, such as “drill a hole into the chair,” but it might not be able to do so repeatedly. In years three to five, the group posited that the robot would be able to pick up tools and learn how to use new tools. It would respond to more complex chained commands in combination with gestures and could detect errors. As a result, the robot would be a more active participant in the manufacturing process. Generally, though, the robot would still be very constrained in its capabilities, and after five years it would still not be a partner for the human furniture maker.

Scenario C: Hospital Service Robotics
Moderator: Candy Sidner
Group Members: Paul Maglio, Candy Sidner, Liz Sonenberg, Tom Wagner, Rong Xiong, Holly Yanco

Description: A large healthcare organization calculates the enormous sums spent in simply moving things—food, laundry, trash, wheelchairs, even



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