Efficient Self-Supervised Data Collection for Offline Robot Learning
Shadi Endrawis,Gal Leibovich,Guy Jacob,Gal Novik,Aviv Tamar,Shadi Endrawis,Gal Leibovich,Guy Jacob,Gal Novik,Aviv Tamar
A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data, using some data collection policy, and then learn from this data to perform various tasks, using offline learning algorithms. Previous work focused on manually designing the data collection policy, and on tasks where suitable policies can easily be designed, such as r...
