Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving
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Nair, Lakshmi Velayudhan
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Abstract
Robots that are situated in the real world are often faced with unforeseen situations that require them to adapt and improvise to be more useful. Particularly in the context of using tools, there may be situations where a robot does not have access to the tools it needs for completing a task. While humans show remarkable improvisation capabilities, similar skills are beyond the scope of robots today. In order to address these scenarios, a resourceful robot should be able to inventively use whatever objects are available to it, in order to replace the missing tool. We refer to this process as “tool macgyvering”. Tool macgyvering can be achieved by either substituting the missing tool with an object (tool substitution) or constructing a replacement tool by combining multiple objects (tool construction). This thesis examines the problem of tool macgyvering, to enable a robot to effectively use available objects to make up for missing tools that are necessary for completing a task. Specifically, this thesis contributes: (1) a formalization of three levels of tool macgyvering that highlights the levels of complexity involved in tool macgyvering problems; (2) novel algorithms for tool construction through shape, material and attachment reasoning, where attachment refers to the different ways in which objects can be combined; (3) a novel algorithm for tool substitution using shape and material reasoning; (4) a novel framework that performs tool macgyvering through arbitration of substitution and construction, to enable a robot to effectively decide the better of the two solutions for completing the task; and (5) a novel algorithm to perform tool macgyvering in task planning, to enable a robot to leverage existing planning algorithms to perform tool macgyvering efficiently.
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2020-09-29
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Dissertation