EchoVLA: Synergistic Declarative Memory for VLA-Driven Mobile Manipulation

TL;DR

EchoVLA introduces a memory-enhanced vision-language-action model for mobile manipulation, integrating scene and episodic memories to improve navigation and manipulation tasks in changing environments.

Contribution

The paper presents EchoVLA, a novel memory-aware VLA model with a human-inspired declarative memory system for mobile manipulation tasks.

Findings

Achieves success rates of 0.52 on manipulation/navigation tasks in simulation.

Outperforms baseline by +0.20 and +0.11 in success rates.

Demonstrates effectiveness in both simulated and real-world environments.

Abstract

Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. However, existing VLAs are mostly confined to short-horizon, table-top manipulation, lacking the memory and reasoning capability required for mobile manipulation, where agents must coordinate navigation and manipulation under changing spatial contexts. In this work, we present EchoVLA, a memory-aware VLA model for mobile manipulation. EchoVLA incorporates a synergistic declarative memory inspired by the human brain, consisting of a scene memory that maintains a collection of spatial-semantic maps and an episodic memory that stores task-level experiences with multimodal contextual features. The two memories are individually stored, updated, and retrieved based on current observations, task history, and instructions, and their retrieved representations are fused via coarse- and fine-grained attention to guide base-arm diffusion policies. To support large-scale training, we further introduce MoMani, an automated benchmark that generates expert-level trajectories through multimodal large language model (MLLM)-guided planning and feedback-driven refinement, supplemented with real-robot demonstrations. Comprehensive simulated and real-world results demonstrate that EchoVLA substantially improves overall performance, e.g., it achieves the highest success rates of 0.52 on manipulation/navigation tasks and 0.31 on mobile manipulation tasks in simulation, exceeding the strong baseline $\pi_{0.5}$ by +0.20 and +0.11, respectively.