Learning Active Force–Torque Based Policy for Sub-mm Localization of Unseen Holes
Author:
Affiliation:
1. State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control, Zhejiang University, Zhejiang, China
2. Advanced Manufacturing Lab, Huawei Technologies, Shenzhen, China
Funder
STI 2030-Major Projects
National Nature Science Foundation of China
Hangzhou Chengxi Sci-tech Innovation Corridor
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10492621/10413898.pdf?arnumber=10413898
Reference20 articles.
1. Multi-Robot Assembly Strategies and Metrics
2. Transferable force-torque dynamics model for peg-in-hole task;Ding,2019
3. Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data
4. Deep reinforcement learning for high precision assembly tasks
5. Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
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