We propose a visual foresight model for pick-and-place rearrangement manipulation which is able to learn efficiently.
Results show that our method enables the robot to autonomously seat the teddy bear on the 12 previously unseen chairs with a very high success rate.
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.
In this paper, we propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration.
State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.
In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
Ranked #12 on 3D Multi-Person Pose Estimation on Panoptic (using extra training data)