Interaction with unstructured groups of objects allows a robot to discover and manipulate novel items in cluttered environments. We present a framework for interactive singulation of individual items from a pile. The proposed framework provides an overall approach for tasks involving operation on multiple objects, such as counting, arranging, or sorting items in a pile. A perception module combined with pushing actions accumulates evidence of singulated items over multiple pile interactions. A decision module scores the likelihood of a single-item pile to a multiple-item pile based on the magnitude of motion and matching determined from the perception module. Three variations of the singulation framework were evaluated on a physical robot for an arrangement task. The proposed interactive singulation method with adaptive pushing reduces the grasp errors on non-singulated piles compared to alternative methods without the perception and decision modules. This work contributes the general pile interaction framework, a specific method for integrating perception and action plans with grasp decisions, and an experimental evaluation of the cost trade-offs for different singulation methods.

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The thumb is essential to the hand's function in grasping and manipulating objects. Previous anthropomorphic robot hands have thumbs that are biologically-inspired but kinematically-simplified. In order to study the biomechanics and neuromuscular control of hand function, an anatomical robotic model of the human thumb is constructed for the anatomically-correct testbed (ACT) hand. This paper presents our ACT thumb kinematic model that unifies a number of studies from biomechanical literature. We also validate the functional consistency (i.e. the nonlinear moment arm values) between the cadaveric data and the ACT thumb. This functional consistency preserves the geometric relationship between muscle length and joint angles, which allows robotic actuators to imitate human muscle functionality.

This paper presents a new direct method for estimating the average center of rotation (CoR). An existing least-squares solution has been shown by previous works to have reduced accuracy for data with small range of motion (RoM). Alternative methods proposed to improve the CoR estimation use iterative algorithms. However, in this paper we show that with a carefully chosen normalization scheme, constrained least-squares solutions can perform as well as iterative approaches, even for challenging problems with significant noise and small RoM. In particular, enforcing the normalization constraint avoids poor fits near plane singularities that can affect the existing least-squares method. Our formulation has an exact solution, accounts for multiple markers simultaneously, and does not depend on manually-adjusted parameters. Simulation tests compare the method to four published CoR estimation techniques. The results show that the new approach has the accuracy of the iterative methods as well as the short computation time and repeatability of a least-squares solution. In addition, application of the new method to experimental motion capture data of the thumb carpometacarpal (CMC) joint yielded a more plausible CoR location compared to the previously reported least-squares solution and required less time than all four alternative techniques.