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.

References

A key unsolved problem in current hierarchical agent research for embodied tasks, including ours, is about the interaction between high-level agents and low-level executors (e.g., VLAs).

— RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems  (2508.01415 - Lei et al., 2 Aug 2025) in Conclusion and Future Work

We have not yet evaluated such an integrated system and leave it to future work.

— Attacca: Goal-Directed Control under State Continuity for Long-Horizon Embodied Agents  (2610.07785 - Seo et al., 6 Oct 2026) in Section Limitations, Appendix

We hypothesise that for cube-double continued high-level shaping increasingly removes distinctions that are irrelevant to high-level value but necessary for fine-grained control; without the low-level Q function to preserve this information, the resulting abstractions become insufficient for the low-level policy.

— Q-Shaped Options for Hierarchical Reinforcement Learning  (2610.12135 - Wibault et al., 8 Oct 2026) in Section 5, paragraph beginning “How necessary is joint option shaping by both the high and low-level Q functions?”

It therefore remains unclear whether VLMs can perform reliable reasoning and planning in embodied scenarios when high-level decisions are separated from low-level execution.

— Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?  (2610.18836 - Feng et al., 13 Oct 2026) in Section 1, Introduction

How-level instruction following thus remains an open challenge.

— Do VLAs Really Follow Instructions? Diagnosing How-Level Instruction Following in Vision-Language-Action Models  (2610.21251 - Park et al., 15 Oct 2026) in Abstract