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 …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

This article deals with the problem of mobile robot path planning in an unknown
environment that contains both static and dynamic obstacles, utilizing a reinforcement …

Nonprehensile rearrangement is the problem of controlling a robot to interact with objects
through pushing actions in order to reconfigure the objects into a predefined goal pose. In …

Self-supervised learning for depth estimation possesses several advantages over
supervised learning. The benefits of no need for ground-truth depth, online fine-tuning, and …

Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

In this letter, we present a planner for manipulating tethered tools using dual-armed robots.
The planner generates robot motion sequences to maneuver a tool and its cable while …

Teaching complex manipulation skills, such as folding garments, to a bi-manual robot is a
very challenging task, which is often tackled through learning from demon-stration. The few …

The manipulation of large objects and safe operation in the vicinity of humans are key
capabilities of a general purpose domestic robotic assistant. We present the design of a soft …

This article presents a planner for a master dual-arm robot to manipulate tethered tools with
the help of an assistant dual-arm robot. The assistant robot assists the master robot by …