Model Predictive Control of Quadruped Robot Based on Reinforcement Learning

Quadruped robot obstacle negotiation via reinforcement learning

Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning algorithm to the problem of negotiating obstacles with a quadruped robot. Our algorithm is based on a two-level hierarchical decomposition of the task, in which the high-level controller selects the sequence of foot-placement position...

Experience-based model predictive control using reinforcement learning

Reinforcement Learning Based PID Control of Wind Energy Conversion Systems

In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

Reinforcement learning for robot control

Writing control code for mobile robots can be a very time-consuming process. Even for apparently simple tasks, it is often difficult to specify in detail how the robot should accomplish them. Robot control code is typically full of “magic numbers” that must be painstakingly set for each environment that the robot must operate in. The idea of having a robot learn how to accomplish a task, rather...

Learning Compliant Locomotion on a Quadruped Robot

Over the last decades, compliant locomotion and manipulation have become a very active field of research, due to the versatility that robots with such capabilities would offer in many applications. With very few exceptions, generally, robotic applications and experiments take place in controlled environments. One of the reasons of this limited use is that in real world scenarios, robots need to...