NSF Grant 0312963: A Robotics-Based Computational Environment to simulate the Human Hand

Prof. Peter Allen
Robotics Lab, Computer Science Department, Columbia University

Computer models for studying the human hand

Robotic hands are still a long way from matching the grasping and manipulation capability of their human counterparts, but computer simulation may help us understand this disparity. We are developing a biomechanically realistic model of the human hand, which can be used in a simulation environment to analyze the hand from a functional point of view. Such a model would also serve to aid clinicians planning reconstructive surgeries of a hand, and creating more effective designs for hand prostheses.
In this project, we have collaborated with the Cornell Neuromuscular Biomechanics Laboratory, who has provided us with detailed anatomical and kinematic data of the human hand, which we have incorproated into some of our models.

A major effort is to focus on the deformation undertaken by soft fingertips during grasping tasks, which greatly increases the ability to create stable grasps using subsets of fingers. Here are examples of dynamic simulations of an anthropomorphic hand using soft fingertips for performing manipulation tasks. In order to achieve computational rates needed for dynamic simulation, we are using an analytical soft finger model that uses general expressions for non-planar contacts of elastic bodies to account for the local geometry and structure of the bodies in contact.

We have also developed a tendon-driven model of the human hand, using anatomical data from medical images and existing biomedical literature (a tendon-driven fingertip model is shown in the bottom image). This model helps to explain the complex relationship going from muscle activation levels to tendon network branch forces, fingertip forces and finally manipulation ability. Here is movie of the tendon model in GraspIt!

By incorporating the soft finger model and tendon driven control, we are better able to simulate a true human hand within our grasping simulator GraspIt! which is currently available for download.

Robotic grasping and manipulation

Another major focus of this project is to extend the capabilities of our GraspIt! simulator, originally built in the Robotics Lab at Columbia. As part of this project, we have extended the grasp analysis features integrated within the GraspIt! simulator to account for soft fingertips, as in the case of robotic hands equipped with fingers coated in a thin layer of rubber (upper image). By using Finite Element Analysis, the deformation of the fingerpad can be computed (bottom image) together with the space of forces and moments that the finger can apply at a contact. By using a finite element based method, results are very accurate from a physical point of view, at the expense of higher computational effort.

Physical-based dynamic simulation

We have developed a Finite Element Method based engine for dynamic simulation of deformable objects (top image shows this engine used to compute the deformation of a box-shaped object under the effect of gravity). This software can be used as a C++ library, and also as a stand-alone application as it includes an OpenGL-based  visualization component. The engine can currently use three simulation methods:
  • Newton iteration method converging to the equilibrium position of the system
  • Newmark step based method, used for dynamic simulations and computing the velocity and accelerations at the vertices of the deformable mesh
  • modified Newton iteration accounting for frictional contacts against planar surfaces. If the direction of relative motion at the contact is specified, this will compute the effects of contact normal forces as well as friction on the vertices of the deformable mesh.
This engine has been used for studying robotic fingertips (as described above) as well as human fingertips. The bottom image shows an anatomically correct fingertip model (with outer surface as well as the inner bone obtained from medical images) deforming under pressure from a planar surface.
Grasp Planning

The ability to plan and execute a realizable and stable grasp on a 3-D object is crucial for many robotics applications, but many grasp planning approaches ignore the relative sizes of the robotic hand and the object being grasped or do not account for physical joint and positioning limitations. We have developed a grasp planner that can consider the full range of parameters of a real hand and an arbitrary object, including physical and material properties as well as environmental obstacles and forces, and produce an output grasp that can be immediately executed without any further computation. We do this by decomposing a 3D model into a superquadric "decomposition tree". We can then use this decomposition tree to prune the intractably large space of possible grasps into a subspace that is likely to contain many good grasps. The parameters of the grasps that lie within this subspace can then be sampled and the results evaluated in GraspIt!, to find a highly stable grasp to output. We have experimental results on a database of object models using a Barrett hand. We have also implemented an SVM classifier that can be used to reduce the number of candidate grasps.
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