提出新方法让机器人在嘈杂环境中仍能准确感知自身体型。
Robust Visual Embodiment: How Robots Discover Their Bodies in Real Environments
- 结合图像修复与结构约束，抑制噪声干扰
- 在模拟与真实机器人上恢复接近基线性能
- 适合部署于复杂现实场景的自感知机器人
具备内部视觉自模型的机器人有望实现前所未有的适应能力，但现有自主建模流程在真实感知条件下（如图像噪声、背景杂乱）仍易失效。本文首次系统量化了模糊、椒盐噪声和高斯噪声对机器人自建模的影响。通过仿真与物理实验，验证其对形态预测、轨迹规划及损伤恢复的负面影响。为此，提出一种任务感知去噪框架，融合经典图像修复与形态保持约束，确保关键结构线索不丢失；同时引入语义分割，从复杂多彩场景中精准分离机器人本体。大量实验表明，该方法在模拟与物理平台上均恢复近基线性能，而现有方法显著退化。研究成果提升了视觉自建模的鲁棒性，为自感知机器人在不可预测真实环境中的部署奠定基础。
原文摘要 · Abstract (English)
Robots with internal visual self-models promise unprecedented adaptability, yet existing autonomous modeling pipelines remain fragile under realistic sensing conditions such as noisy imagery and cluttered backgrounds. This paper presents the first systematic study quantifying how visual degradations--including blur, salt-and-pepper noise, and Gaussian noise--affect robotic self-modeling. Through both simulation and physical experiments, we demonstrate their impact on morphology prediction, trajectory planning, and damage recovery in state-of-the-art pipelines. To overcome these challenges, we introduce a task-aware denoising framework that couples classical restoration with morphology-preserving constraints, ensuring retention of structural cues critical for self-modeling. In addition, we integrate semantic segmentation to robustly isolate robots from cluttered and colorful scenes. Extensive experiments show that our approach restores near-baseline performance across simulated and physical platforms, while existing pipelines degrade significantly. These contributions advance the robustness of visual self-modeling and establish practical foundations for deploying self-aware robots in unpredictable real-world environments.
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