Autonomous navigation at high speeds in off-road environments necessitates robots to
comprehensively understand their surroundings using onboard sensing only. The extreme …

Despite decades of research, existing navigation systems still face real-world challenges
when deployed in the wild, eg, in cluttered home environments or in human-occupied public …

Most conventional wheeled robots can only move in flat environments and simply divide
their planar workspaces into free spaces and obstacles. Deeming obstacles as non …

The high cost of real-world data for robotics Reinforcement Learning (RL) leads to the wide
usage of simulators. Despite extensive work on building better dynamics models for …

Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D
planar workspaces can be divided into free spaces and obstacles. However, recent wheeled …

The Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2022
IEEE International Conference on Robotics and Automation (ICRA), in Philadelphia, PA …

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane,
ie,, such an assumption may not hold when they drive at high speeds on unstructured off …

Wheeled robots have recently demonstrated superior mechanical capability to traverse
vertically challenging terrain (eg, extremely rugged boulders comparable in size to the …

Accurate control of robots at high speeds requires a control system that can take into
account the kinodynamic interactions of the robot with the environment. Prior works on …

Empowering robots to navigate in a socially compliant manner is essential for the
acceptance of robots moving in human-inhabited environments. Previously, roboticists have …