This work contributes to the development of a common framework for the discussion and analysis of dexterous manipulation across the human and robotic domains. An overview of previous work is first provided along with an analysis of the tradeoffs between arm and hand dexterity. A hand-centric and motion-centric manipulation classification is then presented and applied in four different ways. It is first discussed how the taxonomy can be used to identify a manipulation strategy. Then, applications for robot hand analysis and engineering design are explained. Finally, the classification is applied to three activities of daily living (ADLs) to distinguish the patterns of dexterous manipulation involved in each task. The same analysis method could be used to predict problem ADLs for various impairments or to produce a representative benchmark set of ADL tasks. Overall, the classification scheme proposed creates a descriptive framework that can be used to effectively describe hand movements during manipulation in a variety of contexts and might be combined with existing object centric or other taxonomies to provide a complete description of a specific manipulation task.
In this paper, we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of small objects. Taking inspiration from the human grasping strategy for picking up objects from a flat surface, we introduce the flip-and-pinch task, in which the hand picks up a thin object by flipping it into a stable configuration between two fingers. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can interact with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. Even when utilizing only open-loop kinematic playback, this approach is shown to be robust to variation in object size and hand position. Variation of up to 20 in orientation and 10 mm in hand height still result in experimental success rates of 80% or higher. These results suggest that the advantages of underactuated, adaptive robot hands can be carried over from basic grasping tasks to more dexterous tasks. Note to Practitioners-This work was motivated by the need for a means for robots operating in unstructured environments to robustly grasp and manipulate a wide range of objects using a multipurpose hand.To date, one of the most difficult tasks for a general-purpose hand has been grasping small, thin objects, which are typically found resting on a flat surface such as a table. The size of the object and the presence of the backing surface make it difficult to establish contact with the object resulting in a stable grasp. In previous work, we have shown how proper attention to the passive mechanics of the hand, including mechanical compliance and underactuated differential transmissions, can enable robust, open-loop "power" grasping of large objects. In this paper, we extend the same concept to "precision" grasping of small objects with the same demonstrated robustness and simplicity.
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