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Found 11 papers, 6 papers with code
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing.
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model.
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications.
Bayesian Active Learning for Sim-to-Real Robotic Perception
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap.
Unknown Object Segmentation from Stereo Images
This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map.
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
Furthermore, while the motion of the manipulator and the object are substantial cues for our algorithm, we present means to robustly deal with distraction objects moving in the background, as well as with completely static scenes.
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Accurate object segmentation is a crucial task in the context of robotic manipulation.
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization.
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6D Pose Estimation using RGBD
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