Title Studies on control methods for musculoskeletalrobots using muscle synergies

of Thesis Musculoskeletal robots have flexible and compliant structure inspired by biological creatures. They are capable of performing a variety of tasks, and can enhance dexterity and safety in various situations, such as replacing human jobs to perform dangerous and tedious tasks, and environments where robots work in close proximity with human. However, technical difficulties of controlling the complex structure that having many joints and muscles hinders development to practical applications. In biological studies, it has been suggested that the central nervous system of vertebrates simplifies control complexity by coordinating groups of muscle co-activations, namely muscle synergies, to produce movements, instead of controlling muscles independently. This research studies control methods using muscle synergies for musculoskeletal robots. First, in a case of controlling a musculoskeletal robot using an optimal control theory, analysis of several sets of muscle synergies arising from optimizing muscle activations according to different optimization objectives is carried out. Results show that muscle synergies can reduce control dimensionality while maintaining control performance. Moreover, the analysis demonstrates that the muscle synergies for performing a specific task can be extracted from muscle activations optimized according to an energy-related optimization objective function that does not include task-related variables. Second, the problem of how to extract muscle synergies given a data sample of parameterized muscle activations that are randomly initialized, without prior knowledge of robot dynamics is investigated. Most literature assumes that muscle synergies can be directly extracted from a given data sample of muscle activations that have inherent statistical regularities. A data preprocessing method is proposed to estimate a set of muscle activations that produces the same set of end-effector accelerations with minimum control efforts, from the randomly initialized parameterized muscle activations, based on system identification of the robot dynamics using a kernel-based regression technique. A data-driven controller is also designed using sliding mode control technique to perform task space tracking control task. Results show that muscle synergies can be extracted from the estimated set of muscle activations, and can be utilized to control a musculoskeletal robot in a reduced control dimensionality. The proposed method contributes to enabling extraction of muscle synergies from data sample without statistical regularities. Third, the problem of enabling a musculoskeletal robot to obtain muscle synergies by itself is studied. Inspired by the motor skill learning in human infants, a data collection method is proposed based on a goal-directed exploration strategy. During exploration of designated targets spreading over an unknown task space, the robot is controlled in a reduced control dimensionality using muscle synergies and the data-driven task space tracking controller established from a local data sample. Results show that the proposed method can enable the robot to obtain muscle synergies and to establish a low-dimensional controller by itself, making a step forward to the development of autonomous musculoskeletal robots. Finally, this thesis concludes with several current limitations and future directions. The main contribution of this thesis is the investigation of the feasibility of control methods utilizing muscle synergies for a musculoskeletal robot. This research would be the first step to the realization of robots that can work in daily life.