用人类行走示范训练模型，判断地形是否可通行。
Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features
- 基于DINOv2提取像素级特征，通过重建误差判断地形熟悉度。
- 在ANYmal机器人上实测，能有效识别复杂地形中的危险区域。
- 适合需要自主穿越自然地形的腿式机器人使用。
理解地形可通行性对自主机器人导航至关重要，尤其在自然景观等非结构化环境中。传统方法如占用地图虽提供基础框架，却难以反映腿式机器人等平台的复杂移动能力。本文提出一种基于人类行走示范学习的地形可通行性估计方法。利用DINOv2视觉变换器生成密集像素级特征嵌入，经编码器-解码器MLP架构分析地形片段。从感兴趣区域的掩码中提取平均特征向量，在基于重建的框架下训练模型。通过最小化重建误差，网络能区分熟悉地形（低误差）与不熟悉或危险地形（高误差），从而实现异常检测，帮助腿式机器人更有效地穿越复杂地形。我们在ANYmal腿式机器人上进行了室内外真实实验，验证了该方法的有效性。代码已开源，视频演示可见官网：https://rpl-cs-ucl.github.io/STEPP
原文摘要 · Abstract (English)
Understanding the traversability of terrain is essential for autonomous robot navigation, particularly in unstructured environments such as natural landscapes. Although traditional methods, such as occupancy mapping, provide a basic framework, they often fail to account for the complex mobility capabilities of some platforms such as legged robots. In this work, we propose a method for estimating terrain traversability by learning from demonstrations of human walking. Our approach leverages dense, pixel-wise feature embeddings generated using the DINOv2 vision Transformer model, which are processed through an encoder-decoder MLP architecture to analyze terrain segments. The averaged feature vectors, extracted from the masked regions of interest, are used to train the model in a reconstruction-based framework. By minimizing reconstruction loss, the network distinguishes between familiar terrain with a low reconstruction error and unfamiliar or hazardous terrain with a higher reconstruction error. This approach facilitates the detection of anomalies, allowing a legged robot to navigate more effectively through challenging terrain. We run real-world experiments on the ANYmal legged robot both indoor and outdoor to prove our proposed method. The code is open-source, while video demonstrations can be found on our website: https://rpl-cs-ucl.github.io/STEPP
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