This dissertation focuses on developing a fall-tolerant framework for bipedal robots, aiming to enhance their ability to navigate challenging situations by effectively assessing, adapting, and responding to uncertainties and disturbances. Bipedal robots, with their unique capability to navigate diverse terrains and restore mobility, are ideal for assisting in critical and day-to-day tasks. However, their real-world deployment is limited due to factors like high-dimensional complex dynamics and a smaller support polygon, making it difficult to achieve stable motion, especially in the face of disturbances and uncertainties.

The dissertation addresses these limitations by developing robust controllers and reliable fall prediction algorithms. Feedback controllers have been used in the literature to ensure robustness against disturbances and uncertainties. However, it is infeasible to account for all disturbances and uncertainties, making falls inevitable. Falls are undesirable as they can prevent a robot from completing its task, result in damage to the surrounding area, or lead to injuries. Therefore, the dissertation emphasizes the importance of implementing robust controllers and employing methods to predict falls.

This research begins by introducing a systematic method to design control objectives for highly constrained systems and concludes by presenting a 1D convolutional neural network fall prediction algorithm capable of not only predicting falls but also estimating the time to react. The effectiveness of the control objectives is demonstrated through robust, comfortable closed-loop sit-to-stand motions for a fully actuated lower-limb exoskeleton, Atalante. The performance of the proposed fall prediction algorithms is evaluated both in simulation using a planar-four link robot based on Atalante and in hardware and simulation for the bipedal robot Digit.