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 …

The field of robotics has been rapidly developing in recent years, and the work related to
training robotic agents with reinforcement learning has been a major focus of research. This …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

To solve multi-step manipulation tasks in the real world, an autonomous robot must take
actions to observe its environment and react to unexpected observations. This may require …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …

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 …

The majority of robots in factories today are operated with conventional control strategies
that require individual programming on a task-by-task basis, with no margin for error. As an …

Object finding in clutter is a skill that requires perception of the environment and in many
cases physical interaction. In robotics, interactive perception defines a set of algorithms that …

We aim for mobile robots to function in a variety of common human environments. Such
robots need to be able to reason about the locations of previously unseen target objects …

Prehensile object rearrangement in cluttered and confined spaces has broad applications
but is also challenging. For instance, rearranging products in a grocery shelf means that the …