What Stands-in for a Missing Tool? A Prototypical Grounded Knowledge-based Approach to Tool Substitution
This addresses tool substitution for robots in dynamic environments, but it is incremental as it builds on existing knowledge-based methods.
The paper tackles the problem of robots finding substitute tools when required ones are missing, presenting a grounded knowledge-based approach that matched expert choices in 91% of tested scenarios (20 out of 22).
When a robot is operating in a dynamic environment, it cannot be assumed that a tool required to solve a given task will always be available. In case of a missing tool, an ideal response would be to find a substitute to complete the task. In this paper, we present a proof of concept of a grounded knowledge-based approach to tool substitution. In order to validate the suitability of a substitute, we conducted experiments involving 22 substitution scenarios. The substitutes computed by the proposed approach were validated on the basis of the experts' choices for each scenario. Our evaluation showed, in 20 out of 22 scenarios (91%), the approach identified the same substitutes as experts.