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. This paper aims to enable the robots to make informed decisions by quantifying errors in the object pose estimate and acceptable errors for task success.
In this paper, the authors 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 succes.
The authors evaluated the proposed framework on two mobile manipulation tasks. Their results show that by representing the estimated and the acceptable error space using multi-modal non-parametric distributions, the authors achieve higher task success rates and fewer failures.
Their proposed framework is generic and can be applied to a wide range of robotic tasks requiring object pose estimation. Hence, given the recent advancements in object pose uncertainty estimation and dynamic simulations, the proposed framework, in conjunction with these advancements, has the potential to enable robots to make reliable and informed decisions under pose uncertainty.
Unlike related works that model both acceptable error space and estimated error space using parametric uni-modal distributions, the authors model them as multi-modal distributions which is often the case in the real world.
