The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

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 …

Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires
both reasoning about unseen object parts and potential collisions with the manipulator. Most …

Recently, the robotics industry celebrated its 60-year anniversary. We have used robots for
more than six decades to empower people to do things that are typically dirty, dull and/or …

We study the problem of learning to perform multistage robotic manipulation tasks, with
applications to cable routing, where the robot must route a cable through a series of clips …

This paper presents INVIGORATE, a robot system that interacts with human through natural
language and grasps a specified object in clutter. The objects may occlude, obstruct, or even …

We study an emerging problem named “grasping the invisible” in robotic manipulation, in
which a robot is tasked to grasp an initially invisible target object via a sequence of pushing …

The development of future technologies can be highly influenced by our deeper
understanding of the principles that underlie living organisms. The Living Machines …

This letter considers the problem of retrieving an object from many tightly packed objects
using a combination of robotic pushing and grasping actions. Object retrieval in dense …