In robot control, mathematical equations describing dynamic behaviors of robots are usually complicated. Additionally, the components such as inertial and friction parameters appearing in these equations are very difficult to determine exactly. With robots having complex structure such as parallel robots, MRM robots etc., the derivation of dynamic equations becomes more difficult and sometimes cannot obtain analytically. In those cases, controlling robot based on its equations of motion is quite hard. Applying fuzzy logic for robot control can overcome the mentioned drawbacks. This is because fuzzy control algorithm gives favorable condition to deal with the lack of adequation as well as inaccuracy of components in robot's dynamic equations. Furthermore, the fuzzy rules are created by clauses which based on human logic, so it is easily to understand and implement. This paper discusses the application of fuzzy logic for controlling MRM robots. To compare the results obtained from fuzzy control, this paper are also adressed the use of the computed torque algorithm to control MRM robots.

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Objectives:To study an algorithm to control a bipedal robot to walk so that it has a gait close to that of a human. It is known that the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is a highly efficient algorithm with a few changes compared to the popular algorithm -the commonly used Deep Deterministic Policy Gradient (DDPG) in the continuous action space problem in Reinforcement Learning. Methods: Different from the usual sparse reward function model used, in this study, a reward model combined with a sparse reward function and dense reward function will be proposed. The application of the TD3 algorithm together with the proposed reward function model to control a bipedal robot model with 6 degrees of freedom will be presented. The training process is simulated in Gazebo/Robot Operating System (ROS) environment. Finding: The results show that, when choosing a reward model combined with a sparse reward function and a dense reward function suitable for the robot model, will help it learn faster and achieve better results. The biped robot can walk straight with an almost human-like gait. In the paper, the results from the TD3 algorithm combined with the proposed reward model are also compared with the results from other algorithms. Novelty: Applying the TD3 algorithm combined with the proposed reward model for the 6-DOF biped robot and simulating the robot's gait in Gazebo/ROS environment, ROS is a middleware that can be used to control a robot in a real environment in the future.

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