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Found 5 papers, 3 papers with code
SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios
Specifically, at the keypoint prediction stage, we designe a robust 3D keypoints selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes.
Learning visual-based deformable object rearrangement with local graph neural networks
Our method reaches much higher success rates on a variety of deformable rearrangement tasks (96. 3% on average) than state-of-the-art method in simulation experiments.
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Some research has been attempting to design a general framework to obtain more advanced manipulation capabilities for deformable rearranging tasks, with lots of progress achieved in simulation.
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects.
Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion
To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data.
