Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

We present a challenging new benchmark and learning-environment for robot learning:
RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in …

We describe a framework for research and evaluation in Embodied AI. Our proposal is
based on a canonical task: Rearrangement. A standard task can focus the development of …

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 …

Numerical experiments for motion planning of road vehicles require numerous components:
vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement …

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 …

We review the work on data-driven grasp synthesis and the methodologies for sampling and
ranking candidate grasps. We divide the approaches into three groups based on whether …

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 …

Robotic hands have long strived to reach the performance of human hands. The physical
complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation …

The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place
robot for autonomous warehousing, addressing development in robotic vision and …