Deep Reinforcement Learning for Dynamic Locomotion of Humanoid Robots

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The research project is focused on using deep reinforcement learning tosolve dynamic locomotion of humanoid robots. In the past, locomotion ismainly done using conventional analytical approaches, e.g.Model-Predictive Control, which are limited because they require humaneffort and knowledge, and demand high computing power to run online. Incontrast to on-line computation methods, the computation for machinelearning approaches can be outsourced offline. By doing so, fasteronline performance for high dimensional control systems, such ashumanoids, can be achieved. Machine learning approaches such as deepreinforcement learning also requires less human effort to designcompared to analytical approaches.Given the increasingly more powerful deep RL algorithms, an increasingnumber of research works have used deep RL to solve control tasks, asthe recent progress in deep RL algorithms designed for continuous actiondomain has brought forward the possibility to apply reinforcementlearning continuous control tasks that involve complicated dynamics.The project plans to explore the feasibilities of using deepreinforcement learning to acquire bipedal control policies comparable orbetter than analytical approaches while using less human effort. Andeventually design a control framework based on deep RL that is capableof learning a wide range of balancing and walking strategies withminimum human intervention, and compare the performance of the designedcontrol framework with current human labour intensive analyticengineering approach. The project will also explore other methodologiesrelated to reinforcement learning, such as imitation learning andtransfer learning, and implement them into the proposed controlframework.