EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation

Recent progress in Vision–Language–Action (VLA) modelshas enabled embodied agents to interpret multimodal instructions andperform complex tasks. However, existing VLAs are mostly confined toshort-horizon, table-top manipulation, lacking the memory and reasoningcapability required for mobile manipulation, where agents must coordi-nate navigation and manipulation under changing spatial contexts. Inthis work, we present EchoVLA, a memory-aware VLA model for mobilemanipulation. EchoVLA incorporates a synergistic declarative memoryinspired by the human brain, consisting of a scene memory that main-tains a collection of spatial–semantic maps and an episodic memory thatstores task-level experiences with multimodal contextual features. Thetwo memories are individually stored, updated, and retrieved based oncurrent observations, task history, and instructions, and their retrievedrepresentations are fused via coarse- and fine-grained attention to guidebase–arm diffusion policies. To support large-scale training, we furtherintroduce MoMani, an automated benchmark that generates expert-leveltrajectories through multimodal large language model (MLLM)–guidedplanning and feedback-driven refinement, supplemented with real-robotdemonstrations. Comprehensive simulated and real-world results demon-strate that EchoVLA substantially improves overall performance, e.g., itachieves the highest success rates of 0.52 on manipulation/navigationtasks and 0.31 on mobile manipulation tasks in simulation, exceedingthe strong baseline π0.5 by +0.20 and +0.11, respectively.