IEEE Robotics & Automation Magazine

Citations in the corpus

• Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, background): “A random region in this image is then selected to be the region of interest for the specific trial instance.”
• Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, background): “2) [156].”
• Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" (2016, methodology): “, 2009] as implemented in the Point Cloud Library [Aldoma et al., 2012]; and 3) deep visual features from the 16-layer VGG network [Simonyan and Zisserman, 2014].”
• A Survey of Research on Cloud Robotics and Automation (2015, background): “It envisioned “a World Wide Web for robots: a giant network and database repository where robots can share information and learn from each other about their behavior and environment” [22], [156] as illustrated in Fig.”