6-DoF Grasp Pose Evaluation and Optimization via Transfer Learning from NeRFs
Author:
Affiliation:
1. Karlsruhe University of Applied Sciences,Institute of Robotics and Autonomous Systems,Karlsruhe,Germany,76133
2. Karlsruhe Institute of Technology,Institute for Anthropomatics and Robotics,Karlsruhe,Germany,76131
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10609961/10609862/10610402.pdf?arnumber=10610402
Reference31 articles.
1. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
2. Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations
3. Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
4. Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications
5. On Scene Engineering and Domain Randomization: Synthetic Data for Industrial Item Picking
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