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…
By Ben Kehoe, Sachin Patil, Pieter Abbeel, Ken Goldberg. 2014
Subfields: 3D Vision & Pose, Robotics & Control
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In 3D Vision & Pose
In Robotics & Control
- Mrinal Kalakrishnan
- Sergey Levine
- Timothy P. Lillicrap
- Yevgen Chebotar
- Shixiang Shane Gu
- Ali Yahya
- Adrian Shuai Li
- Chelsea Finn
Citations in the corpus
- Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2016, background): “Accelerating robotic learning by pooling experience from multiple robots has long been recognized as a promising direction in the domain of cloud robotics, where it is typically referred to as collective robotic learning [31], [32], [33], [34].”
- Collective robot reinforcement learning with distributed asynchronous guided policy search (2016, background): “The ability of robotic systems to learn more quickly and effectively by pooling their collective experience has long been recognized in the domain of cloud robotics, where it is typically referred to as collective robotic learning [11, 12, 13, 14].”
- Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “of workload for VMs on cloud has been carried out by [65] [68] [69].”
- Internet of vehicles in big data era (2018, background): “The IoV data security and privacy issues can also be a big concern for autonomous vehicles, as investigated in [136]−[138].”
- Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards (2016, unknown): “Potential advantages to using the Cloud include Big Data: access to updated libraries of images, maps, and object/product data; and Parallel Computation: access to grid computing for statistical analysis, machine learning, and planning [22].”