Gradient based Grasp Pose Optimization on a NeRF that Approximates Grasp Success
Current robotic grasping methods often rely on estimating the pose of the target object, explicitly predicting grasp poses, or implicitly estimating grasp success probabilities. In this work, we propose a novel approach that directly maps gripper poses to their corresponding grasp success values, wi...
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Zusammenfassung: | Current robotic grasping methods often rely on estimating the pose of the
target object, explicitly predicting grasp poses, or implicitly estimating
grasp success probabilities. In this work, we propose a novel approach that
directly maps gripper poses to their corresponding grasp success values,
without considering objectness. Specifically, we leverage a Neural Radiance
Field (NeRF) architecture to learn a scene representation and use it to train a
grasp success estimator that maps each pose in the robot's task space to a
grasp success value. We employ this learned estimator to tune its inputs, i.e.,
grasp poses, by gradient-based optimization to obtain successful grasp poses.
Contrary to other NeRF-based methods which enhance existing grasp pose
estimation approaches by relying on NeRF's rendering capabilities or directly
estimate grasp poses in a discretized space using NeRF's scene representation
capabilities, our approach uniquely sidesteps both the need for rendering and
the limitation of discretization. We demonstrate the effectiveness of our
approach on four simulated 3DoF (Degree of Freedom) robotic grasping tasks and
show that it can generalize to novel objects. Our best model achieves an
average translation error of 3mm from valid grasp poses. This work opens the
door for future research to apply our approach to higher DoF grasps and
real-world scenarios. |
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DOI: | 10.48550/arxiv.2309.08040 |