SEVO：语义增强的虚拟观察用于通过主动照明和数据导向收集的鲁棒VLA操作
SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection
AI总结：
SEVO通过主动照明和数据导向收集提升跨环境操作鲁棒性，采用三机制改进观察，实现95%和83%的成功率，在新环境中转移率达85%和75%。
摘要：
Vision-Language-Action (VLA)和通过社区工具链在低成本硬件上训练的模仿学习策略在部署到训练环境外时经常失败。现有的评估，包括原始ACT和SmolVLA基准，显示在受控固定背景下成功率高，但社区实践者报告几乎无法转移到新环境。我们提出了SEVO（语义增强的虚拟观察），一种数据导向的方法，通过三种机制改进原始RGB摄像头流：（1）固定身体摄像头，其组合视场覆盖整个操作工作区；（2）主动红光照明，物理上归一化物体外观；（3）实时YOLO分割叠加，提供背景不变的语义提示。关键的是，我们证明了一种多样化数据收集协议（在远程操作中系统地变化照明、背景和干扰因素）是通用化最重要的因素。我们针对透明水瓶，这些物体在视觉上与周围环境融合，并选择一个简单的拾取和放置任务，以在两个移动平台上进行数百次受控真实机器人试验。完整的流程在训练环境中实现95%的抓取成功率（ACT）和83%（SmolVLA），在新环境中转移率达85%和75%。没有SEVO，相同的策略在训练中仅实现75%/70%，在新环境中崩溃到30-35%。我们的结果表明，原则性的观察设计和数据收集期间的环境多样性，而不是模型扩展，使低成本机器人能够在日常家庭环境中可靠运行。
英文摘要：
Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside the training environment. Existing evaluations, including the original ACT and SmolVLA benchmarks, demonstrate high success rates under controlled, fixed backgrounds, yet community practitioners report near-zero transfer to new environments. We present SEVO (Semantic-Enhanced Virtual Observation), a data-centric approach that improves cross-environment manipulation robustness without modifying the policy architecture. SEVO transforms the raw RGB camera stream through three mechanisms: (1) body-fixed cameras whose combined fields of view cover the full manipulation workspace, (2) active red-spectrum illumination that physically normalizes object appearance, and (3) real-time YOLO segmentation overlay that provides a background-invariant semantic cue. Critically, we show that a diversified data collection protocol (systematically varying lighting, backgrounds, and distractors during teleoperation) is the single most important factor for generalization. We target transparent water bottles, objects that visually blend with their surroundings, and select a simple pick-and-place task to enable hundreds of controlled real-robot trials across two mobile platforms. The full pipeline achieves 95% grasp success with ACT and 83% with SmolVLA in the training environment, transferring to novel environments at 85% and 75%. Without SEVO, the same policies achieve only 75%/70% in training and collapse to 30-35% in novel environments. Our results demonstrate that principled observation design and environmental diversity during data collection, not model scaling, enable low-cost robots to operate reliably in everyday household environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
