Moving in complex environments is an essential capability of intelligent mobile robots.
Decades of research and engineering have been dedicated to developing sophisticated …

This letter presents a learning-based approach to consider the effect of unobservable world
states in kinodynamic motion planning in order to enable accurate high-speed off-road …

Autonomous driving in unstructured environments is crucial for various applications,
including agriculture, military, and mining. However, research in unstructured environments …

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness,
deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through …

Existing autonomous robot navigation systems allow robots to move from one point to
another in a collision-free manner. However, when facing new environments, these systems …

While current autonomous navigation systems allow robots to successfully drive themselves
from one point to another in specific environments, they typically require extensive manual …

Classical autonomous navigation systems can control robots in a collision-free manner,
oftentimes with verifiable safety and explainability. When facing new environments …

Classical navigation systems typically operate using a fixed set of hand-picked parameters
(eg maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning …

Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous
navigation, which uses training data collected in completely safe environments and adds …

While decades of research efforts have been devoted to developing classical autonomous
navigation systems to move robots from one point to another in a collision-free manner …