The Power of Hybrid Learning in Industrial Robotics: Efficient Grasping Strategies with Supervised-Driven Reinforcement Learning
Author:
Affiliation:
1. Politecnico di Milano,AIRLAB, SIEMENS,Milan,Italy
2. Politecnico di Milano,AIRLAB,Milan,Italy
3. Politecnico di Milano,AIRLAB,Milano,Italy
Funder
Siemens
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650627.pdf?arnumber=10650627
Reference31 articles.
1. Deep spatial autoencoders for visuomotor learning
2. Embodied foundation models for large scale orchestration of robotic agents;Ahn
3. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
4. Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
5. Deep visual foresight for planning robot motion
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