MonoForce: Self-supervised Learning of Physics-aware Model for Predicting Robot-terrain Interaction
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要