GraSP-VLA: Graph-based Symbolic Action Representation for Long-Horizon Planning with VLA Policies

Understand

Deploying autonomous robots that can learn new skills from demonstrations is an important challenge of modern robotics.

• Existing solutions often apply end-to-end imitation learning with Vision-Language Action (VLA) models or symbolic approaches with Action Model Learning (AML).
• On the one hand, current VLA models are limited by the lack of high-level symbolic planning, which hinders their abilities in long-horizon tasks.
• On the other hand, symbolic approaches in AML lack generalization and scalability perspectives.