## Accelerating the construction of boundaries of feasibility in three classes of robot design problems

Shervin Ghasemlou and **Jason M. O'Kane**

In *Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems*

2019
**Abstract**
This paper aims to improve the practical scalability of automated tools to
assist in designing robots. Such problems rapidly become intractable
because the underlying design space is immense. We consider a specific type
of design tool addressed in prior work, which constructs a representation
of the destructiveness boundary in the space of robot designs. This prior
work showed that a legible representation, specifically a decision tree, of
this boundary can illuminate which elements of a sensing or actuation
system are most important for enabling the robot to complete its task. In
that context, the robot's interaction with the world is represented as
procrustean graph, and the space of robot designs is represented by the
space of label maps that rewrite the labels on that graph. In this paper,
we expand upon those results by showing how domain knowledge can enable
such tools to find solutions to more complex problems within a reasonable
time frame. Specifically, we propose three different scenarios, expressed
as constraints on the p-graph and on the label maps, under which the
learning algorithm to identify the destructiveness boundary can converge
quickly to high accuracy results for problems at larger scales than the
prior, general-purpose algorithm. The conditions for each of these
scenarios are easily verifiable and the set of problems that fall under
each is rich enough to encompass several interesting problems. Experimental
results demonstrate the effectiveness of the proposed methods.

@inproceedings{GhaOKa19,
author = {Shervin Ghasemlou and Jason M. O'Kane},
booktitle = {Proc. IEEE/RSJ International Conference on Intelligent
Robots and Systems},
title = {Accelerating the construction of boundaries of feasibility
in three classes of robot design problems},
year = {2019}
}

*Last updated 2024-07-02.*