3 accepted papers
This paper reports on learning a reward map for social navigation in dynamic environments where the robot can reason about its path at any time, given agent trajectories and scene geometry. Humans navigating in dense and dynamic indoor environments often work with several implied social rules. A rul
Affine Grassmannian has been favored for expressing proximity between lines and planes due to its theoretical exactness in measuring distances among features. Despite this advantage, the existing method can only measure the proximity without yielding the distance as an explicit function of rigid bod…
Controlling marine vehicles in challenging environments is a complex task due to the presence of nonlinear hydrodynamics and uncertain external disturbances. Despite nonlinear model predictive control (MPC) showing potential in addressing these issues, its practical implementation is often constrain