SwitchVLA：无需额外数据采集，即可实时动态任务切换的轻量化VLA模型

Core Viewpoint - The article introduces SwitchVLA, a lightweight and data-efficient method for dynamic task perception and decision-making, addressing the challenges of task switching in multi-task VLA models, achieving superior performance compared to existing methods [3][22]. Group 1: Introduction - Current mainstream multi-task VLA models struggle with task switching, defined as "Task Switching," where the model's ability to adapt to new tasks mid-execution is limited [3][5]. - SwitchVLA employs an Execution-Aware mechanism and a lightweight network architecture to facilitate task switching without the need for additional data collection [3][10]. Group 2: Background - Multi-task VLA training typically involves independent data collection for each task, leading to challenges in seamlessly transitioning between tasks [5]. - The inability of existing SOTA VLA methods to effectively handle task switching is highlighted, emphasizing the need for improved solutions [5][10]. Group 3: Methodology - SwitchVLA addresses two core problems: representing task switching without extra data collection and training an end-to-end imitation learning model that autonomously judges based on current conditions [10][12]. - The model improves task switching representation by concatenating previous task, current task, and the previous task's stage, enhancing the model's ability to perceive task transitions [12][13]. - A simplified training process categorizes tasks into three stages: before contact, during contact, and after contact, allowing for effective task switching without additional data [15][16]. Group 4: Experimental Results - Experiments demonstrate that SwitchVLA outperforms existing methods in task switching scenarios while maintaining comparable performance in single-task settings [20][22]. - The analysis of task switching failures reveals that the proposed method effectively mitigates common failure causes [20]. Group 5: Conclusion and Future Directions - SwitchVLA is positioned as a significant advancement in dynamic task management, with plans for further iterations and deployment in humanoid robots for applications in flexible industrial production and personalized commercial services [22][23].