Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse … [Read more...] about Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
Reinforcement learning
Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method
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
Reinforcement-Learning-Based Local Search Approach to Integrated Order Batching: Driving Growth for Logistics and Retail
As an important part of Industry 4.0, a smart warehouse can offer smart tips and operational constraints for users. Improving its work efficiency is a promising growth driver for logistics companies and retailers. Therefore, a reinforcement-learning-based adaptive iterated local search (RAILS) approach is proposed to improve order-picking efficiency for a smart warehouse. A … [Read more...] about Reinforcement-Learning-Based Local Search Approach to Integrated Order Batching: Driving Growth for Logistics and Retail
Simulation to Real: Learning Energy-Efficient Slithering Gaits for a Snake-Like Robot
To resemble the body flexibility of biological snakes, snake-like robots are designed as a chain of body modules, which gives them many degrees of freedom (DoF) on the one hand and leads to a challenging task to control them on the other. Compared with conventional model-based control methods, reinforcement learning (RL)-based ones provide promising solutions to design agile … [Read more...] about Simulation to Real: Learning Energy-Efficient Slithering Gaits for a Snake-Like Robot
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
