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 ...

Abstract: Although the traditional robot arm grasping control has high control accuracy, its price is based on high-precision hardware and lacks flexibility. In order to achieve high control accuracy ...

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 ...

Based on the TensorFlow framework, a data-driven model is established, and the data-driven model is trained using deep reinforcement learning strategy to realize posture control of a single soft arm.

ANYmal-D, the badminton-playing quadruped robot from ETH Zurich, combines artificial intelligence with whole-body ...

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 ...