Robot navigation under visual corruption presents a formidable challenge. To
address this, we propose a Test-time Adaptation (TTA) method, named as TTA-Nav,
for point-goal navigation under visual corruptions. Our "plug-and-play" method
incorporates a top-down decoder to a pre-trained navigation model. Firstly, the
pre-trained navigation model gets a corrupted image and extracts features.
Secondly, the top-down decoder produces the reconstruction given the high-level
features extracted by the pre-trained model. Then, it feeds the reconstruction
of a corrupted image back to the pre-trained model. Finally, the pre-trained
model does forward pass again to output action. Despite being trained solely on
clean images, the top-down decoder can reconstruct cleaner images from
corrupted ones without the need for gradient-based adaptation. The pre-trained
navigation model with our top-down decoder significantly enhances navigation
performance across almost all visual corruptions in our benchmarks. Our method
improves the success rate of point-goal navigation from the state-of-the-art
result of 46% to 94% on the most severe corruption. This suggests its potential
for broader application in robotic visual navigation.

通过测试时适应 (TTA) 方法，我们的研究提出了一种名为 TTA-Nav 的机器人视觉导航方法，在视觉失真条件下进行目标导向的导航。我们的方法通过将自顶向下解码器与预训练导航模型结合，从失真图像中提取特征，重建较干净的图像，并使导航模型能够在各种视觉失真环境中显著提高导航性能。该方法将目标导向的导航成功率从最先进结果的 46% 提高到 94%，表明其在机器人视觉导航领域具有广泛应用的潜力。

TTA-Nav: 测试时自适应恢复点目标导航在视觉损坏下

TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation  under Visual Corruptions

We exploit the complementary strengths of vision and proprioception to
develop a point-goal navigation system for legged robots, called VP-Nav. Legged
systems are capable of traversing more complex terrain than wheeled robots, but
to fully utilize this capability, we need a high-level path planner in the
navigation system to be aware of the walking capabilities of the low-level
locomotion policy in varying environments. We achieve this by using
proprioceptive feedback to ensure the safety of the planned path by sensing
unexpected obstacles like glass walls, terrain properties like slipperiness or
softness of the ground and robot properties like extra payload that are likely
missed by vision. The navigation system uses onboard cameras to generate an
occupancy map and a corresponding cost map to reach the goal. A fast marching
planner then generates a target path. A velocity command generator takes this
as input to generate the desired velocity for the walking policy. A safety
advisor module adds sensed unexpected obstacles to the occupancy map and
environment-determined speed limits to the velocity command generator. We show
superior performance compared to wheeled robot baselines, and ablation studies
which have disjoint high-level planning and low-level control. We also show the
real-world deployment of VP-Nav on a quadruped robot with onboard sensors and
computation. Videos at this https URL

利用视觉和本体感底层控制策略开发了一种针对四足机器人的点目标导航系统（VP-Nav），使用本体感反馈确保计划路径的安全性来实现对机械的信息感知。通过使用内置摄像头生成占有空间图和相应的代价图，以到达目标，在速度指令生成器中设置安全顾问模块来对意外障碍进行感知和环境决定的速度限制。

机器人步态导航中的视觉与本体感融合

Coupling Vision and Proprioception for Navigation of Legged Robots

Recent work has presented embodied agents that can navigate to point-goal
targets in novel indoor environments with near-perfect accuracy. However, these
agents are equipped with idealized sensors for localization and take
deterministic actions. This setting is practically sterile by comparison to the
dirty reality of noisy sensors and actuations in the real world -- wheels can
slip, motion sensors have error, actuations can rebound. In this work, we take
a step towards this noisy reality, developing point-goal navigation agents that
rely on visual estimates of egomotion under noisy action dynamics. We find
these agents outperform naive adaptions of current point-goal agents to this
setting as well as those incorporating classic localization baselines. Further,
our model conceptually divides learning agent dynamics or odometry (where am
I?) from task-specific navigation policy (where do I want to go?). This enables
a seamless adaption to changing dynamics (a different robot or floor type) by
simply re-calibrating the visual odometry model -- circumventing the expense of
re-training of the navigation policy. Our agent was the runner-up in the
PointNav track of CVPR 2020 Habitat Challenge.

本文介绍了一种基于视觉位移估计和任务特定导航策略的点目标导航代理，该代理可适应嘈杂的传感器和动作动态，并在 CVPR 2020 Habitat Challenge 的 PointNav 跟踪中获得亚军。