2 research outputs found
Tool Macgyvering: A Novel Framework for Combining Tool Substitution and Construction
Macgyvering refers to solving problems inventively by using whatever objects
are available at hand. Tool Macgyvering is a subset of macgyvering tasks
involving a missing tool that is either substituted (tool substitution) or
constructed (tool construction), from available objects. In this paper, we
introduce a novel Tool Macgyvering framework that combines tool substitution
and construction using arbitration that decides between the two options to
output a final macgyvering solution. Our tool construction approach reasons
about the shape, material, and different ways of attaching objects to construct
a desired tool. We further develop value functions that enable the robot to
effectively arbitrate between substitution and construction. Our results show
that our tool construction approach is able to successfully construct working
tools with an accuracy of 96.67%, and our arbitration strategy successfully
chooses between substitution and construction with an accuracy of 83.33%.Comment: NOTE: This paper is currently under review for IEEE Transactions on
Robotics (T-RO). IEEE copyright notice applie
Feature Guided Search for Creative Problem Solving Through Tool Construction
Robots in the real world should be able to adapt to unforeseen circumstances.
Particularly in the context of tool use, robots may not have access to the
tools they need for completing a task. In this paper, we focus on the problem
of tool construction in the context of task planning. We seek to enable robots
to construct replacements for missing tools using available objects, in order
to complete the given task. We introduce the Feature Guided Search (FGS)
algorithm that enables the application of existing heuristic search approaches
in the context of task planning, to perform tool construction efficiently. FGS
accounts for physical attributes of objects (e.g., shape, material) during the
search for a valid task plan. Our results demonstrate that FGS significantly
reduces the search effort over standard heuristic search approaches by
approximately 93% for tool construction.Comment: NOTE: This paper has been published with Frontiers in Robotics and
AI. Please see the following link for the most updated version:
https://www.frontiersin.org/articles/10.3389/frobt.2020.592382/ful