In the field of robotic manipulator operations, precise trajectory planning for the end-
effector's position and orientation is crucial, especially in tasks such as grasping a bottle by …

In recent years, the motion planning algorithm based on reinforcement learning has shown
its potential to meet the requirements of high-dimensional planning space, complex task …

Motion planning and control is a necessary aspect of incorporating robots into the real world.
There are a variety of different types of control tasks that involve collision avoidance and fine …

Control of robotics for object grasping and manipulation is still a complex problem with many
different approaches and solutions. This project examines the usage of actor-critic …

Since path planning for multi-arm manipulators is a complicated high-dimensional problem,
effective and fast path generation is not easy for the arbitrarily given start and goal locations …

This paper proposes a task decomposition and dedicated reward-system-based
reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level …

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of
the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult …

This paper presents a Nonlinear Model Predictive Control-based Reinforcement Learning
(NMPC-based RL) framework for robot manipulators. The controller is developed to address …

Recently, deep reinforcement learning (DRL)-based trajectory planning methods have been
designed for manipulator trajectory planning, given their potential in solving the problem of …

In a dynamic environment, the moving obstacle makes the path planning of the manipulator
very difficult. Therefore, this paper proposes a path planning with dynamic obstacle …