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The ability to recover from an unexpected external perturbation is afundamental motor skill in bipedal locomotion. An effective response includesthe ability to not just recover balance and maintain stability but also to fallin a safe manner when balance recovery is physically infeasible. For robotsassociated with bipedal locomotion, such as humanoid robots and assistiverobotic devices that aid humans in walking, designing controllers which canprovide this stability and safety can prevent damage to robots or preventinjury related medical costs. This is a challenging task because it involvesgenerating highly dynamic motion for a high-dimensional, non-linear andunder-actuated system with contacts. Despite prior advancements in usingmodel-based and optimization methods, challenges such as requirement ofextensive domain knowledge, relatively large computational time and limitedrobustness to changes in dynamics still make this an open problem. In thisthesis, to address these issues we develop learning-based algorithms capable ofsynthesizing push recovery control policies for two different kinds of robots :Humanoid robots and assistive robotic devices that assist in bipedallocomotion. Our work can be branched into two closely related directions : 1)Learning safe falling and fall prevention strategies for humanoid robots and 2)Learning fall prevention strategies for humans using a robotic assistivedevices. To achieve this, we introduce a set of Deep Reinforcement Learning(DRL) algorithms to learn control policies that improve safety while usingthese robots.

Learning Control Policies for Fall prevention and safety in bipedal locomotion