Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space
Kuan Fang,Patrick Yin,Ashvin Nair,Sergey Levine,Kuan Fang,Patrick Yin,Ashvin Nair,Sergey Levine
General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach configurable goals for a wide range of tasks on command. However, such goal-conditioned policies are notoriously difficult and time-consuming to train from scratc...
