CARE：通过斥力估计的碰撞避免增强视觉导航安全性
CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation
AI总结：
提出CARE模块，通过RGB估计深度并计算斥力向量调整轨迹，无需微调或额外传感器，显著降低视觉导航碰撞率并提升探索距离。
摘要：
我们提出CARE（通过斥力估计的碰撞避免），以提高基于学习的视觉导航方法的鲁棒性。近年来，视觉导航模型，特别是基础模型，通过仅使用RGB图像生成可行轨迹，展示了有前景的性能。然而，这些策略在面对包含分布外（OOD）场景的环境时可能泛化不佳，这些场景以未见过的物体或不同的相机设置（例如，视场角、相机姿态或焦距的变化）为特征。在没有微调的情况下，此类模型可能产生导致碰撞的轨迹，从而需要在数据收集和额外训练方面付出大量努力。为解决这一局限，我们引入CARE，一个可附加模块，在不需额外距离传感器或预训练模型微调的情况下增强视觉导航的安全性。CARE可以无缝集成到任何生成局部机器人轨迹的基于RGB的导航模型中。它使用直接从RGB输入估计的深度图像计算出的斥力向量，动态调整预训练模型生成的轨迹。我们通过将CARE与多种机器人平台上的最先进视觉导航模型集成来评估其性能。真实世界实验表明，CARE在目标条件导航中显著减少碰撞（最高达100%），且不损害导航性能，并在探索任务中进一步改善无碰撞行驶距离（最高达10.7倍）。项目页面：https://airlab-sogang.github.io/CARE/
英文摘要：
We propose CARE (Collision Avoidance via Repulsive Estimation) to improve the robustness of learning-based visual navigation methods. Recently, visual navigation models, particularly foundation models, have demonstrated promising performance by generating viable trajectories using only RGB images. However, these policies can generalize poorly to environments containing out-of-distribution (OOD) scenes characterized by unseen objects or different camera setups (e.g., variations in field of view, camera pose, or focal length). Without fine-tuning, such models could produce trajectories that lead to collisions, necessitating substantial efforts in data collection and additional training. To address this limitation, we introduce CARE, an attachable module that enhances the safety of visual navigation without requiring additional range sensors or fine-tuning of pretrained models. CARE can be integrated seamlessly into any RGB-based navigation model that generates local robot trajectories. It dynamically adjusts trajectories produced by a pretrained model using repulsive force vectors computed from depth images estimated directly from RGB inputs. We evaluate CARE by integrating it with state-of-the-art visual navigation models across diverse robot platforms. Real-world experiments show that CARE significantly reduces collisions (up to 100%) without compromising navigation performance in goal-conditioned navigation, and further improves collision-free travel distance (up to 10.7x) in exploration tasks. Project page: https://airlab-sogang.github.io/CARE/
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。
