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Found 5 papers, 1 papers with code
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
Ranked #1 on
Unseen Object Instance Segmentation
on WISDOM
Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact.
Robotics
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.
Robotics
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.
