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Found 6 papers, 5 papers with code
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark
We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation.
Camera-to-Robot Pose Estimation from a Single Image
We show experimental results for three different camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is better than that of classic off-line hand-eye calibration using multiple frames.
Robotics
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data.
Robotics
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
We present a system to infer and execute a human-readable program from a real-world demonstration.
Robotics
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
We present a system for training deep neural networks for object detection using synthetic images.
Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation
We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics.
