  Accurate 6D object pose estimation is essential for various robotic tasks.
Uncertain pose estimates can lead to task failures; however, a certain degree
of error in the pose estimates is often acceptable. Hence, by quantifying
errors in the object pose estimate and acceptable errors for task success,
robots can make informed decisions. This is a challenging problem as both the
object pose uncertainty and acceptable error for the robotic task are often
multi-modal and cannot be parameterized with commonly used uni-modal
distributions. In this paper, we introduce a framework for evaluating robotic
task success under object pose uncertainty, representing both the estimated
error space of the object pose and the acceptable error space for task success
using multi-modal non-parametric probability distributions. The proposed
framework pre-computes the acceptable error space for task success using
dynamic simulations and subsequently integrates the pre-computed acceptable
error space over the estimated error space of the object pose to predict the
likelihood of the task success. We evaluated the proposed framework on two
mobile manipulation tasks. Our results show that by representing the estimated
and the acceptable error space using multi-modal non-parametric distributions,
we achieve higher task success rates and fewer failures.
