Agnostic Policy

Agnostic policy learning aims to develop robotic control policies that generalize across diverse tasks or environments without requiring retraining for each specific scenario. Current research focuses on creating policies that are transferable across different robotic grippers, adaptable to various dynamic scheduling tasks in IoT networks, and capable of sequencing multiple learned skills to achieve complex, long-horizon goals. This involves leveraging techniques like federated reinforcement learning, transformer networks, and hierarchical approaches combining imitation and offline reinforcement learning, often incorporating latent skill representations. The resulting advancements promise more robust, efficient, and adaptable robotic systems for a wide range of applications.