Ruimeng Yang

Shengyu Si, Yuanzhuo Lu, Ruimeng Yang et al.

Vision-Language-Action~(VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate transferring relevant experience across objects, scenes, and action patterns. This paper proposes VLA-Pro, a plug-and-play framework designed to enhance cross-task generalization by storing task-relevant procedural memories at training time and transferring these memories during inference. Specifically, VLA-Pro stores task-specific LoRA adapters as parameterized procedural memories during training. At inference time, VLA-Pro retrieves relevant procedural memories based on the current multi-modal context and dynamically fuses these memories for generating the current action chunk. Experiments on RoboTwin, RLBench, and real-world manipulation tasks show that VLA-Pro consistently improves cross-task generalization across multiple backbones, achieving up to a 207% relative improvement in simulation and increasing real-world success rate from 5.8% to 65.0%. These results suggest that procedural memory retrieval and adaptation provide an effective mechanism for transferring manipulation experience to novel tasks while preserving modularity and execution stability.

Jiazheng Sun, Te Yang, Jiayang Niu et al.

Large multi-modal models (LMMs) have advanced mobile GUI agents. However, existing methods struggle with real-world scenarios involving diverse app interfaces and evolving user needs. End-to-end methods relying on model's commonsense often fail on long-tail apps, and agents without user interaction act unilaterally, harming user experience. To address these limitations, we propose Fairy, an interactive multi-agent mobile assistant capable of continuously accumulating app knowledge and self-evolving during usage. Fairy enables cross-app collaboration, interactive execution, and continual learning through three core modules:(i) a Global Task Planner that decomposes user tasks into sub-tasks from a cross-app view; (ii) an App-Level Executor that refines sub-tasks into steps and actions based on long- and short-term memory, achieving precise execution and user interaction via four core agents operating in dual loops; and (iii) a Self-Learner that consolidates execution experience into App Map and Tricks. To evaluate Fairy, we introduce RealMobile-Eval, a real-world benchmark with a comprehensive metric suite, and LMM-based agents for automated scoring. Experiments show that Fairy with GPT-4o backbone outperforms the previous SoTA by improving user requirement completion by 33.7% and reducing redundant steps by 58.5%, showing the effectiveness of its interaction and self-learning.