A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
A review of recent trend in motion planning of industrial robots
MG Tamizi, M Yaghoubi, H Najjaran - International Journal of Intelligent …, 2023 - Springer
Motion planning is an integral part of each robotic system. It is critical to develop an effective
motion in order to achieve a successful performance. The ability to generate a smooth …
motion in order to achieve a successful performance. The ability to generate a smooth …
Reducing collision checking for sampling-based motion planning using graph neural networks
Sampling-based motion planning is a popular approach in robotics for finding paths in
continuous configuration spaces. Checking collision with obstacles is the major …
continuous configuration spaces. Checking collision with obstacles is the major …
Nerp: Neural rearrangement planning for unknown objects
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary
ways as they become more widely used in human environments. As such, the …
ways as they become more widely used in human environments. As such, the …
Language-conditioned path planning
Contact is at the core of robotic manipulation. At times, it is desired (eg manipulation and
grasping), and at times, it is harmful (eg when avoiding obstacles). However, traditional path …
grasping), and at times, it is harmful (eg when avoiding obstacles). However, traditional path …
MPC-MPNet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent
kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and …
kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and …
Goal distance-based UAV path planning approach, path optimization and learning-based path estimation: GDRRT*, PSO-GDRRT* and BiLSTM-PSO-GDRRT
The basic conditions for mobile robots to be autonomous are that the mobile robot localizes
itself in the environment and knows the geometric structure of the environment (map). After …
itself in the environment and knows the geometric structure of the environment (map). After …
A survey of learning‐based robot motion planning
A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …
Recently, learning‐based motion‐planning methods have shown significant advantages in …
Bimanual regrasping for suture needles using reinforcement learning for rapid motion planning
Regrasping a suture needle is an important yet time-consuming process in suturing. To
bring efficiency into regrasping, prior work either designs a task-specific mechanism or …
bring efficiency into regrasping, prior work either designs a task-specific mechanism or …
Usa-net: Unified semantic and affordance representations for robot memory
In order for robots to follow open-ended instructions like “go open the brown cabinet over the
sink,” they require an understanding of both the scene geometry and the semantics of their …
sink,” they require an understanding of both the scene geometry and the semantics of their …