Moreover, we successfully perform the placing task by relying only upon image-based input in which the robot arm robustly places 8.8(exp +0.2) −0.61 of nine objects on average into the container, ...

Project Overview This project is based on Deep Reinforcement Learning (DRL) methods, combined with the improved TD3 algorithm (including TD3, TD3(GRU), TD3(LSTM), and TD3(GRU)_PER versions), to ...

The project focuses on controlling a robotic arm, particularly a one-degree-of-freedom joint, to perform the inverted pendulum swing-up task. The goal is to manipulate the joint effectively by ...

This paper introduces a reinforcement-learning-based method to control an industrial robotic arm. The goal is to improve the process of teaching the robotic arm to perform a full-scan of the surface ...

For the case of robotic pick-and-place, the “state” fed to the robotic agent is the RGBD rendered image of the 6 DoF robotic arm in the environment. The “action” is the x, y, z, pitch, roll, yaw, and ...

Collaborative robots are designed to operate around humans in a factory environment and to execute tasks in a human-like fashion.Interestingly, assembly tasks that are simple for humans to perform ...

The current hype about reinforcement learning around robotic applications has a valid motivation, ... robots learn a direct mapping from states to actions. In value-function-based approaches, robots ...

New dual-arm robot achieves bimanual tasks by learning from simulation. ScienceDaily . Retrieved May 8, 2025 from www.sciencedaily.com / releases / 2023 / 08 / 230824003859.htm ...