Dream to Pose in a Tendon-Driven Manipulator with Muscle Synergy

Abstract

Bio-inspired tendon-driven manipulators have the potential to achieve human-level dexterity. However, their control is more complex than prevailing robotic hands because the relation between actuation and hand motion (Jacobian) is hard to obtain. On the other hand, humans maneuver their complex hands skillfully and conduct adaptive object grasping and manipulation. We conjecture that the foundation of this ability is a visual posing of hands (i.e., a skill to make arbitrary hand poses with visual and proprioceptive feedback). Children develop this skill before or in parallel with learning grasping and manipulation. Inspired by this developmental process, this study explored a method to equip compliant tendon-driven manipulators with the visual posing. To overcome the complexity of the system, we used a learning-based approach. Specifically, we adopted PlaNet, model-based reinforcement learning that leverages a dynamics model on a compact latent representation. To further accelerate learning, we restricted the control space using the idea of muscle synergy found in the human body control. We validated the effectiveness of the proposed method in a simulation. We also demonstrated that the posing skill acquired using our method is useful for object grasping. This study will contribute to achieving human-level dexterity in manipulations.