Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT,
and advanced sensors converge to shape smarter future factories. Mobile manipulators …

We describe a learning-based approach to hand-eye coordination for robotic grasping from
monocular images. To learn hand-eye coordination for grasping, we trained a large …

In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be
used for benchmarking in robotic grasping and manipulation research. The objects in the set …

There has been an increasing interest in mobile manipulators that are capable of performing
physical work in living spaces worldwide, corresponding to an aging population with …

In this article, we present the Yale-Carnegie Mellon University (CMU)-Berkeley (YCB) object
and model set, intended to be used to facilitate benchmarking in robotic manipulation …

A key requirement for seamless human-robot collaboration is for the robot to make its
intentions clear to its human collaborator. A collaborative robot's motion must be legible, or …

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on
unifying graph-and sampling-based planning techniques. By recognizing that a set of …

Since the introduction and widespread adoption of the Robot Operating System (ROS), the
mobile manipulation and mobile service robot communities have seen great advances in …

In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be
used to facilitate benchmarking in robotic manipulation, prosthetic design and rehabilitation …