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Although tendon-driven anthropomorphic robot hands have the potential to achieve human-level dexterity, controlling them is a great challenge owing to their mechanical complexities. Therefore, investigating human-hand control strategies is of the utmost importance. An important skill that enables the versatile manipulation ability of humans is  visual posturing , i.e. the skill to make arbitrary hand postures based solely on visual observation.  Visual posturing  facilitates manipulation learning by enabling visual imitation learning and reusing visually similar past experiences. Therefore, this study investigates a method to replicate  visual posturing  in anthropomorphic robotic hands.  Visual posturing  in tendon-driven hands is challenging because of the hysteresis in tendon systems, the partial observability of the problem, and the presence of many actuators owing to the antagonistic tendon arrangement. To address these challenges, we propose a method that combines a model predictive path integral, a world model, and bio-inspired muscle synergies. The evaluation in a physical tendon-driven anthropomorphic robot hand showed that the proposed method achieved better  visual posturing  performance than a naive regression model. We anticipate that our  visual posturing  method will lay the foundation for versatile manipulation controllers that can adaptively learn manipulation tasks, similar to humans.

Dream to posture: visual posturing of a tendon-driven hand using world model and muscle synergies