Neural network position and orientation control of an inverted pendulum on wheels In this contribution, we develop a feedback controller for a wheeled inverted pendulum in the form of a  :18,that is not only stabilizing the unstable system, but  the wheeled :28,robot to drive to arbitrary positions  :37,certain radius and take a desired orientation, without  to :46,compute a feasible trajectory to the desired  :55,While some techniques from the reinforcement  :62,can be used to optimize the parameters of a  :72,controller, i.e. policy gradient methods, the  in this :79,work is an approach related to imitation learning  from :90,demonstration. The demonstration data  not :96,result from e.g. a human demonstrator, but is a  :107,precomputed optimal trajectories. The neural network  to :114,imitate the behavior of those optimal trajectories.  :18,that a :123,good choice of initial states and a large number  :135,targets :62,can be used to alleviate a problem of  :145,namely deviating from training trajectories, and  :152,results in simulation as well as on the 

Model Learning for TwoWheeled Robot SelfBalance Control Twowheeled robots have many advantages over other mobile robots, but they are difficult to selfbalance compared with other wheeled robots. Reinforcement learning (RL) is a general framework for sequential decisionmaking problems. So far, there are many applications of reinforcement learning to solve robot control problems, but most of them are used in simulators because of the large amount of data required. In addition, due to the reality gap, the policy learned in a simulation environment cannot be transferred directly to a real robot. Real robot data often expensive due to the potential damage to the robot. Modelbased methods require far fewer robot data than modelfree methods, but these methods have the problem of model bias. In this paper, we use a modelbased reinforcement learning method to achieve selfbalance of a twowheeled robot. We present a model learning method that can reduce the problem of model bias. Our method combines the simulator and a few real robot data to learn a probabilistic dynamics model of the robot through an iterative way, which requires no expertise and can learn from scratch. Then the control policy is optimized based on the learned model.