In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance,
and planning of the underactuated dynamics of the system. Reliably optimizing for such …

We present iSDF, a continual learning system for real-time signed distance field (SDF)
reconstruction. Given a stream of posed depth images from a moving camera, it trains a …

In this letter, we propose a robust and efficient quadrotor motion planning system for fast
flight in three-dimensional complex environments. We adopt a kinodynamic path searching …

Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

In this article, we present an optimization-based framework for multicopter trajectory
planning subject to geometrical configuration constraints and user-defined dynamic …

Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored environments require
fast and flexible local planning, which can replan when new parts of the map are explored …

This article presents a novel method for exactly reformulating nondifferentiable collision
avoidance constraints into smooth, differentiable constraints using strong duality of convex …

Ensuring that autonomous vehicles do not cause accidents remains a challenge. We
present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic …