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 …

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

We present Orbit, a unified and modular framework for robot learning powered by Nvidia
Isaac Sim. It offers a modular design to easily and efficiently create robotic environments …

To achieve successful field autonomy, mobile robots need to freely adapt to changes in their
environment. Visual navigation systems such as Visual Teach and Repeat (VT&R) often …

Despite impressive dexterous manipulation capabilities enabled by learning-based
approaches, we are yet to witness widespread adoption beyond well-resourced …

Dexterous and autonomous robots should be capable of executing elaborated dynamical
motions skillfully. Learning techniques may be leveraged to build models of such dynamic …

Variable impedance control in operation-space is a promising approach to learning contact-
rich manipulation behaviors. One of the main challenges with this approach is producing a …

We consider the problem of learning motion policies for acceleration-based robotics systems
with a structured policy class specified by RMPflow. RMPflow is a multi-task control …

Optimization fabrics are a geometric approach to real-time local motion generation, where
motions are designed by the composition of several differential equations that exhibit a …

The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints
such as joint limits, self-collisions, and collisions with the environment remains a challenge …