Exploring Pose-Guided Imitation Learning for Robotic Precise Insertion
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
Recent studies have proved that imitation learning shows strong potential in
the field of robotic manipulation. However, existing methods still struggle
with precision manipulation task and rely on inefficient image/point cloud
observations. In this paper, we explore to introduce SE(3) object pose into
imitation learning and propose the pose-guided efficient imitation learning
methods for robotic precise insertion task. First, we propose a precise
insertion diffusion policy which utilizes the relative SE(3) pose as the
observation-action pair. The policy models the source object SE(3) pose
trajectory relative to the target object. Second, we explore to introduce the
RGBD data to the pose-guided diffusion policy. Specifically, we design a
goal-conditioned RGBD encoder to capture the discrepancy between the current
state and the goal state. In addition, a pose-guided residual gated fusion
method is proposed, which takes pose features as the backbone, and the RGBD
features selectively compensate for pose feature deficiencies through an
adaptive gating mechanism. Our methods are evaluated on 6 robotic precise
insertion tasks, demonstrating competitive performance with only 7-10
demonstrations. Experiments demonstrate that the proposed methods can
successfully complete precision insertion tasks with a clearance of about 0.01
mm. Experimental results highlight its superior efficiency and generalization
capability compared to existing baselines. Code will be available at
https://github.com/sunhan1997/PoseInsert.
