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Found 6 papers, 1 papers with code
HIO-SDF: Hierarchical Incremental Online Signed Distance Fields
HIO-SDF combines the advantages of these representations using a hierarchical approach which employs a coarse voxel grid that captures the observed parts of the environment together with high-resolution local information to train a neural network.
Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction
In this paper, we present a novel method to provide this geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously.
Self-supervised Wide Baseline Visual Servoing via 3D Equivariance
Existing approaches that regress absolute camera pose with respect to an object require 3D ground truth data of the object in the forms of 3D bounding boxes or meshes.
Learning Continuous Cost-to-Go Functions for Non-holonomic Systems
In this work, we show that uniform sampling fails for non-holonomic systems.
Cost-to-Go Function Generating Networks for High Dimensional Motion Planning
The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace.
Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning
In this paper, we introduce a new probabilistically safe local steering primitive for sampling-based motion planning in complex high-dimensional configuration spaces.
Robotics
