A hybrid A* search over SE(3) point cloud transforms, with learned suggesters proposing which object to move and where, solves multi-object rearrangement without discretizing actions.
6D Robotic Assembly Based on RGB-only Object Pose Estimation
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abstract
Vision-based robotic assembly is a crucial yet challenging task as the interaction with multiple objects requires high levels of precision. In this paper, we propose an integrated 6D robotic system to perceive, grasp, manipulate and assemble blocks with tight tolerances. Aiming to provide an off-the-shelf RGB-only solution, our system is built upon a monocular 6D object pose estimation network trained solely with synthetic images leveraging physically-based rendering. Subsequently, pose-guided 6D transformation along with collision-free assembly is proposed to construct any designed structure with arbitrary initial poses. Our novel 3-axis calibration operation further enhances the precision and robustness by disentangling 6D pose estimation and robotic assembly. Both quantitative and qualitative results demonstrate the effectiveness of our proposed 6D robotic assembly system.
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Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement
A hybrid A* search over SE(3) point cloud transforms, with learned suggesters proposing which object to move and where, solves multi-object rearrangement without discretizing actions.