2026
Flex: End-to-End Text-Instructed Visual Navigation From Foundation Model Features
RA-L 2026
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models often struggle to generalize beyond their training scenarios, limiting adaptability to new environments, tasks, and concepts. In this work,