Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions
arxiv(2024)
摘要
Manipulating unseen objects is challenging without a 3D representation, as
objects generally have occluded surfaces. This requires physical interaction
with objects to build their internal representations. This paper presents an
approach that enables a robot to rapidly learn the complete 3D model of a given
object for manipulation in unfamiliar orientations. We use an ensemble of
partially constructed NeRF models to quantify model uncertainty to determine
the next action (a visual or re-orientation action) by optimizing
informativeness and feasibility. Further, our approach determines when and how
to grasp and re-orient an object given its partial NeRF model and re-estimates
the object pose to rectify misalignments introduced during the interaction.
Experiments with a simulated Franka Emika Robot Manipulator operating in a
tabletop environment with benchmark objects demonstrate an improvement of (i)
14
reconstruction of the object surface (F-score) and (iii) 71
success rate of manipulating objects a-priori unseen orientations/stable
configurations in the scene; over current methods. The project page can be
found here: https://actnerf.github.io.
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