Apples, oranges, and angles: Comparative kinematic analysis of disparate limbs.

Tetrapod limbs exhibit diverse postures and movements during terrestrial locomotion. As with morphological traits, the history of kinematic evolution should be accessible to reconstruction through analysis of limb motion patterns in a phylogenetic framework. However, the angular data comprising most kinematic descriptions appear to suffer from limitations that preclude meaningful comparison among disparate species. Using simple planar models, we discuss how geometric constraints render joint and elevation angles independent of neither morphology, degree of crouch, nor one another during the stance phase of locomotion. The implicit null hypothesis of potential similarity is invalidated because angular data are not viably transferable among limbs of dissimilar proportion and/or degree of crouch. Overlooking or dismissing the effect of constraints on angular parameterization hampers efforts to quantitatively elucidate the evolution of locomotion. We advocate a search for alternative methods of measuring limb movement that can decouple intersegmental coordination from morphology and posture.

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands.

There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs.

Effect of Object and Task Properties on Bimanual Transport.

The use of both hands simultaneously when manipulating objects is fairly commonplace, but it is not known what factors encourage people to use two hands as opposed to one during simple tasks such as transport. In particular, we are interested in three possible transport strategies: unimanual transport, handing off between hands, and symmetric bimanual transport. In this study, we investigate the effect of object size, weight, and starting and ending position (configuration) as well as the need to balance the object on the use of these three strategies in a bowl-moving task. We find that configuration and balance have a strong effect on choice of strategy, and size and weight have a weaker effect. Hand-offs are most often used when the task requires moving an object from left to right and vice versa, while the unimanual strategy was frequently used when passing front to back. The bimanual strategy is only weakly affected by configuration. The need to balance an object causes subjects to favor unimanual and bimanual strategies over the hand-off. In addition, an analysis of transport duration and body rotation suggests that strategy choice may be driven by the desire to minimize body rotation.

Constrained least-squares optimization for robust estimation of center of rotation.

This paper presents a new direct method for estimating the average center of rotation (CoR). An existing least-squares (LS) 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 LS 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 LS solution and required less time than all four alternative techniques.

Robust estimation of dominant axis of rotation.

A simple method is developed for robustly estimating a fixed dominant axis of rotation (AoR) of anatomical joints from surface marker data. Previous approaches which assume a model of circular marker trajectories use plane fitting to estimate the direction of the AoR. However, when there is limited joint range of motion and rotation due to a second degree of freedom, minimizing only the planar error can give poor estimates of the AoR direction. Optimizing a cost function which includes the error component within a plane, instead of only the component orthogonal to a plane, leads to improved estimates of the AoR direction for joints which exhibit additional rotational motion from a second degree of freedom. Results from synthetic data validation show the ranges of motion where the new method has lower estimation error compared to plane-fitting techniques. Estimates of the flexion-extension AoR from empirical motion capture data of the knee and index finger joints were also more anatomically plausible.

Data-driven grasp synthesis using shape matching and task-based pruning.

Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.

Selection criteria for preparatory object rotation in manual lifting actions.

Participants lifted a canister by its handle while balancing a ball on the lid. Experiment 1 allowed object rotation prior to lifting. A lifting comfort zone was measured by the variability in object orientation at lift; its size depended on the object mass and required task precision. The amount of prelift rotation correlated with the resulting change in lifting capability, as measured for different object orientations. Experiment 2 required direct grasping without preparatory rotation. Task completion time and success rate decreased, and initial object orientation affected prelift time. Results suggest that lifting from the comfort zone produces more robust performance at a cost of slower completion; moreover, physical rotation could be replaced by mental planning when direct grasping is enforced.

Method for determining kinematic parameters of the in vivo thumb carpometacarpal joint.

The mobility of the thumb carpometacarpal (CMC) joint is critical for functional grasping and manipulation tasks. We present an optimization technique for determining from surface marker measurements a subject-specific kinematic model of the in vivo CMC joint that is suitable for measuring mobility. Our anatomy-based cost metric scores a candidate joint model by the plausibility of the corresponding joint angle values and kinematic parameters rather than only the marker trajectory reconstruction error. The proposed method repeatably determines CMC joint models with anatomically-plausible directions for the two dominant rotational axes and a lesser range of motion (RoM) for the third rotational axis. We formulate a low-dimensional parameterization of the optimization domain by first solving for joint axis orientation variables that then constrain the search for the joint axis location variables. Individual CMC joint models were determined for 24 subjects. The directions of the flexion--extension (FE) axis and adduction--abduction (AA) axis deviated on average by 9 degrees and 22 degrees , respectively, from the mean axis direction. The average RoM for FE, AA, and pronation--supination (PS) joint angles were 76 degrees , 43 degrees , and 23 degrees for active CMC movement. The mean separation distance between the FE and AA axes was 4.6 mm, and the mean skew angle was 87 degrees from the positive flexion axis to the positive abduction axis.