The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

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 …

With the rapid development of machine learning, its powerful function in the machine vision
field is increasingly reflected. The combination of machine vision and robotics to achieve the …

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL
models typically involves in a multi-step process: pre-training RL models on simulators …

Robotic manipulation refers to how robots intelligently interact with the objects in their
surroundings, such as grasping and carrying an object from one place to another. Dexterous …

3D scene synthesis facilitates and benefits many real-world applications. Most scene
generators focus on making indoor scenes plausible via learning from training data and …

We present a system for non-prehensile manipulation that require a significant number of
contact mode transitions and the use of environmental contacts to successfully manipulate …

For tabletop object manipulation tasks, learning an accurate pushing dynamics model,
which predicts the objects' motions when a robot pushes an object, is very important. In this …

We propose a novel approach for automatically generating a move plan for scene
arrangement. 1 Given a scene like an apartment with many furniture objects, to transform its …