Interaction mechanisms between high-level agents and low-level executors in hierarchical embodied systems

Determine effective interaction mechanisms between high-level planning agents and low-level Vision-Language-Action executors in hierarchical embodied agent frameworks, particularly when language-only action instructions are insufficient to convey fine-grained control details such as grasp points; identify representations and communication modalities that enable robust, generalizable guidance from the planner to the executor in real-world tasks.

Background

RoboMemory adopts a two-layer architecture where a high-level agent produces abstract plans and a Vision-Language-Action (VLA) model executes low-level controls. While the framework focuses on lifelong memory and closed-loop planning, the authors observe a performance gap in real-world deployment largely attributable to limitations in the low-level executor and to interface constraints between planning and execution.

The paper explicitly highlights that most hierarchical frameworks use language-only instructions to pass actions from the high-level agent to the VLA, which can be inadequate for specifying fine-grained details (e.g., grasp points). The authors identify this interface design as a key unsolved problem and suggest that future work explore multimodal communication mechanisms to improve generalization and execution robustness.