While autonomous navigation of mobile robots on rigid terrain is a well-explored problem, navigating on deformable terrain such as tall grass or bushes remains a challenge. To address it, we introduce an explainable, physics-aware and end-to-end differentiable model which predicts the outcome of robot-terrain interaction from camera images, both on rigid and non-rigid terrain. The proposed MonoForce model consists of a black-box module which predicts robot-terrain interaction forces from onboard cameras, followed by a white-box module, which transforms these forces and a control signals into predicted trajectories, using only the laws of classical mechanics. The differentiable white-box module allows backpropagating the predicted trajectory errors into the black-box module, serving as a self-supervised loss that measures consistency between the predicted forces and ground-truth trajectories of the robot. Experimental evaluation on a public dataset and our data has shown that while the prediction capabilities are comparable to state-of-the-art algorithms on rigid terrain, MonoForce shows superior accuracy on non-rigid terrain such as tall grass or bushes. To facilitate the reproducibility of our results, we release both the code and datasets.

翻译：尽管移动机器人在刚性地形上的自主导航已是一个被充分研究的问题，但在高草丛或灌木丛等可变形地形上的导航仍具挑战。为解决这一问题，我们提出了一种可解释、具物理感知且端到端可微分的模型，该模型能从相机图像中预测机器人-地形交互的结果，适用于刚性和非刚性地形。所提出的MonoForce模型包含一个黑盒模块（用于从车载相机预测机器人-地形交互力）和一个白盒模块（仅利用经典力学定律，将这些力与控制信号转化为预测轨迹）。可微分的白盒模块允许将预测轨迹误差反向传播至黑盒模块，形成一种自监督损失，用于衡量预测力与机器人真实轨迹之间的一致性。在公开数据集及我们收集的数据上的实验评估表明：在刚性地形上，MonoForce的预测能力与现有最先进算法相当；而在高草丛或灌木丛等非刚性地形上，其精度显著更优。为促进结果的可复现性，我们公开了代码与数据集。