Deep reinforcement learning (DRL) has achieved great success across multiple fields; however, in the field of robot control, the acquisition of large amounts of motion data from real robots is challenging. In this work, an algorithm is proposed to train a neural network model with a large amount of data in a simulated environment and then transfer the model to the real … [Read more...] about Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method
Neural networks
Learning Fast and Precise Pixel-to-Torque Control: A Platform for Reproducible Research of Learning on Hardware
In the field, robots often need to operate in unknown and unstructured environments, where accurate sensing and state estimation (SE) become a major challenge. Cameras have been used with great success in mapping and planning in such environments [1] as well as complex but quasi-static tasks, such as grasping [2], but are rarely integrated into the control loop for unstable … [Read more...] about Learning Fast and Precise Pixel-to-Torque Control: A Platform for Reproducible Research of Learning on Hardware
