Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

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

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …

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

Skilled robotic manipulation benefits from complex synergies between non-prehensile (eg
pushing) and prehensile (eg gras**) actions: pushing can help rearrange cluttered objects …

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input
image. The training data consists of a texture-mapped 3D object model or images of the …

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation,
spanning a range of everyday household chores such as cleaning, maintenance, and food …

This article reports on the state of the art of artificial hands, discussing some of the field's
most important trends and suggesting directions for future research. We review and group …

Robot warehouse automation has attracted significant interest in recent years, perhaps most
visibly in the Amazon Picking Challenge (APC)[1]. A fully autonomous warehouse pick-and …

This paper presents an overview of the inaugural Amazon Picking Challenge along with a
summary of a survey conducted among the 26 participating teams. The challenge goal was …