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得到充分研究，但在可变形地形（如高草丛或灌木丛）中的导航仍具挑战性。为解决这一问题，我们提出了一种可解释、物理感知且端到端可微的模型，能够从摄像机图像中预测机器人与刚性及非刚性地形交互的结果。所提出的MonoForce模型包含一个黑箱模块（用于从车载摄像头预测机器人-地形交互力）以及一个白箱模块（仅利用经典力学定律，将这些力与控制信号转换为预测轨迹）。可微的白箱模块允许将预测轨迹误差反向传播至黑箱模块，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采集的数据上进行的实验表明，尽管在刚性地形上的预测能力与现有最优算法相当，但MonoForce在非刚性地形（如高草丛或灌木丛）上展现出更优的精度。为促进结果的可复现性，我们开源了代码与数据集。