This paper presents a robust physics-based motion control system for realtime synthesis of human grasping. Given an object to be grasped, our system automatically computes physics-based motion control that advances the simulation to achieve realistic manipulation with the object. Our solution leverages prerecorded motion data and physics-based simulation for human grasping. We first introduce a data-driven synthesis algorithm that utilizes large sets of prerecorded motion data to generate realistic motions for human grasping. Next, we present an online physics-based motion control algorithm to transform the synthesized kinematic motion into a physically realistic one. In addition, we develop a performance interface for human grasping that allows the user to act out the desired grasping motion in front of a single Kinect camera. We demonstrate the power of our approach by generating physics-based motion control for grasping objects with different properties such as shapes, weights, spatial orientations, and frictions. We show our physics-based motion control for human grasping is robust to external perturbations and changes in physical quantities.

Robust realtime physics-based motion control for human grasping

Factors affecting human hand grasp type in tomato fruit-picking: A statistical investigation for ergonomic development of harvesting robot

Common patterns of voluntary grasp types according to object shape, size, and direction

Automatically generating humanlike grasp postures of the digital hand is a key issue for the virtual ergonomic assessment of the industrial products. In this paper we propose a new optimization-based approach for generating the realistic grasp posture. As an objective function, we use the number of the contact points, the fit of the specific part of the hand surface for the feature edges of the product surface and the margin for the constraints on the joint angle limits of the figures. The experimental studies on the grasp posture generation for the digital camera indicate that more realistic grasp posture could be generated using the proposed optimization-based method than the one using our former method.

Optimization-Based Grasp Posture Generation Method of Digital Hand for Virtual Ergonomics Assessment

We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.

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Physics-based hand interaction with virtual objects

Human-like grasp planning is difficult because a human hand has a high number of degrees of freedom, and there are many grasping styles depending on the shape of an object and purpose We propose a fast grasp synthesis system which enables a user to choose the desired grasping styles from a set of grasp types in a human grasp taxonomy Given a 3D model of an object, our system detects graspable positions and generates grasping hand postures in every applicable grasp types in the grasp taxonomy for each grasping position Hand postures are generated separately for each digit, and hand alignment is then refined iteratively A user can also specify the grasping position by moving the cursor onto object surface, as well as grasp type The generated hand postures are shown as a table of thumbnail images, and the user can select the grasping hand posture by clicking on one of those postures Our system enables interactive generation of various grasping hand postures in real time © 2012 Wiley Periodicals, Inc

Fast Grasp Synthesis for Various Shaped Objects

Detection and evaluation of grasping positions

In-between creation in traditional cel animation based on the hand-drawn key-frames is a fundamental element for the actual production and plays a symbolic role in an artistic interpretation of the scene. To create impressive in-betweens, however, animators are required to be skilled for hair animation creation. In the traditional cel animation, hair motions are generally used to express a character's affective change or showing environment condition. Despite this usability and importance, the hair motion is drawn relatively simply or is not animated at all because of the lack of skilled animators and time constraints in cel animation production. To assist this production process, P. Noble and W. Tang [Noble and Tang. 2004], and Sugisaki et al. [Sugisaki et al. 2006] introduced certain ways to create hair motion for cartoon animations. Both of them created the hair motion based on 3D simulation that is applied to the prepared 3D character model. In this paper, we introduce an in-between creation method, specialized for hair based on dynamic simulation, which does not need any 3D character model. Animators can create in-between frames for hair motion by setting a few parameters, and then our method automatically select the best in-between frames based on the specified frame number by animator. The advantage of our method is to create in-between frames for hair motion by applying simulation model to key-frames. Obviously, the key-frame images do not have any depth. In fact, our method can directly utilize the hand-drawn key-frames which are drawn by animators in CACAni (Computer-Assisted Cel Animation) system [CACAni Website].

Fumihito Kyota

Papers

Fast Grasp Synthesis for Various Shaped Objects

Detection and evaluation of grasping positions

Simulation-based in-between creation for CACAni system