Learning task-specific models for dexterous, in-hand manipulation with simple, adaptive robot hands

In this paper, we propose a hybrid methodology based on a combination of analytical, numerical and machine learning methods for performing dexterous, in-hand manipulation with simple, adaptive robot hands. A constrained optimization scheme utilizes analytical models that describe the kinematics of adaptive hands and classic conventions for modelling quasistatically the manipulation problem, providing intuition about the problem mechanics. A machine learning (ML) scheme is used in order to split the problem space, deriving task-specific models that account for difficult to model, dynamic phenomena (e.g., slipping). In this respect, the ML scheme: 1) employs the simulation module in order to explore the feasible manipulation paths for a specific hand-object system, 2) feeds the feasible paths to an experimental setup that collects manipulation data in an automated fashion, 3) uses clustering techniques in order to group together similar manipulation trajectories, 4) trains a set of task-specific manipulation models and 5) uses classification techniques in order to trigger a task-specific model based on the user provided task specifications. The efficacy of the proposed methodology is experimentally validated using various adaptive robot hands in 2D and 3D in-hand manipulation tasks.