Database guided computer animation of human grasping using forward and inverse kinematics

doi:10.1016/S0097-8493(98)00122-8

Computers & Graphics

Volume 23, Issue 1, 1 February 1999, Pages 145-154

https://doi.org/10.1016/S0097-8493(98)00122-8 Get rights and content

Abstract

This paper addresses the important issue of automating grasping movement in the animation of virtual actors, and presents a methodology and algorithm to generate realistic looking grasping motion of arbitrary shaped objects. A hybrid approach using both forward and inverse kinematics is proposed. A database of predefined body postures and hand trajectories are generalized to adapt to a specific grasp. The reachable space is divided into small subvolumes, which enables the construction of the database. The paper also addresses some common problems of articulated figure animation. A new approach for body positioning with kinematic constraints on both hands is described. An efficient and accurate manipulation of joint constraints is also presented. Finally, we describe an interpolation algorithm which interpolates between two postures of an articulated figure by moving the end effector along a specific trajectory and maintaining all the joint angles in the feasible range. Results are quite satisfactory, and some are shown in the paper.

References (0)

Cited by (49)

A visually realistic grasping system for object manipulation and interaction in virtual reality environments
2019, Computers and Graphics (Pergamon)
Citation Excerpt :
Finally, some conclusions are drawn in the last Section 8. Grasping action is the most basic component of any interaction and it is composed of three major components [2]. The first one is related to the process of approaching the arm and hand to the target object, considering the overall body movement.
Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting grasps are visually realistic because hand is automatically fitted to the object shape from a position and orientation determined by the user using the VR handheld controllers (e.g. Oculus Touch motion controllers). Our approach is flexible because it can be adapted to different hand meshes (e.g. human or robotic hands) and it is also easily customizable. Moreover, it enables interaction with different objects regardless their geometries. In order to validate our proposal, an exhaustive qualitative and quantitative performance analysis has been carried out. On one hand, qualitative evaluation was used in the assessment of abstract aspects, such as motor control, finger movement realism, and interaction realism. On the other hand, for the quantitative evaluation a novel metric has been proposed to visually analyze the performed grips. Performance analysis results indicate that previous experience with our grasping system is not a prerequisite for an enjoyable, natural and intuitive VR interaction experience.
Data-driven humanlike reaching behaviors synthesis
2016, Neurocomputing
Citation Excerpt :
Here we select some representative literatures to explain this kind of work briefly. Aydin and Nakajima [1] use the forward and inverse kinematics to generate the grasping motion with a large motion database. Raunhardt and Boulic [2] deal with the reaching motion synthesis in low-dimensional data space and utilize the numerical iterative method to refine the final result.
Reaching is one of the most important behaviors in our daily life and has attracted plenty of researchers to work on it both in computer animation and robot research area. However, existing proposed methods either lack of flexibility or their results are not so convincing. In this paper, we present a novel controller-based framework for reaching motion synthesis. Our framework consists of four stationary controllers to generate concrete reaching motion and three transition controllers to stitch these stationary controllers automatically. For each stationary controller, it can either be applied alone or combined with other stationary controllers. Due to this design, our method can imitate the inherent tentative process for human reaching effectively. And our controller is able to generate continuous reaching motion based on virtual character׳s previous status with no need to start from one same initial pose. Moreover, we involve an important gaze simulation model into each controller, which can guarantee the consistency between the head and hand movement. The experiments show that our framework is very easy to be implemented and can generate natural-looking reaching motion in real-time.
Modeling Visually Guided Hand Reach for Digital Human Models
2015, Procedia Manufacturing
Digital Human Models (DHM) are used in digital mockups to identify human factor problems in design and assembly. Present applications are mostly limited to the posture, biomechanics, reach and simple visibility analysis and they are mostly developed for independent simulations that are mostly hand crafted by designers. Our aim is to develop natural simulations using vision as a feedback agent for performing any postural simulations similar to humans. In this paper, the work presented is limited to demonstrating active vision based feedback for a typical hand reach task without using inverse kinematics. The proposed concept takes into account of previously developed vision and hand modules and describes an integration methodology such that both modules can work in tandem providing feedback and feed forward mechanisms. The scheme primarily utilizes vision module that acts similar to human eyes by providing spatial information about hand and object in workspace. Similar to retinal projection, the workspace object and the model of DHM hand is geometrically projected over the grid and the relative positions are computed in terms of grid-cells. The computed relative positions are used to compute a vector direction that is provided as a feedback to hand for guiding it towards object. The hand module independently is capable of natural grasping and visual feedback is used for motion guidance. The implementation shown in this paper is limited to monocular vision and two-dimensional hand movement as a proof of concept. This scheme is used to demonstrate a scenario where DHM is successfully able guide the hand and point it to a given object. The presented model finally shows vision as a guiding agent for hand reach simulations. It can be used for planning and placement of workspace objects to enhance human task performance.
A Side of Data with My Robot
2015, Household Service Robotics
The consideration of data set design, collection, and distribution methodology is becoming increasingly important as robots move out of fully controlled settings, such as assembly lines, into unstructured environments. Extensive knowledge bases and data sets will potentially offer a means of coping with the variability inherent in the real world. In this study, we introduce three new data sets related to mobile manipulation in human environments. The first set contains a large corpus of robot sensor data collected in typical office environments. Using a crowd-sourcing approach, this set has been annotated with ground-truth information outlining people in camera images. The second data set consists of three-dimensional (3-D) models for a number of graspable objects commonly encountered in households and offices. Using a simulator, we have identified on each of these objects a large number of grasp points for a parallel jaw gripper. This information has been used to attempt a large number of grasping tasks using a real robot. The third data set contains extensive proprioceptive and ground truth information regarding the outcome of these tasks
Smooth Hand Preshaping for Visually Realistic Grasping in Virtual Reality
2023, Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023
Grasping 3D Objects With Virtual Hand in VR Environment
2022, Proceedings - VRCAI 2022: 18th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry

View all citing articles on Scopus

View full text

Technical SectionDatabase guided computer animation of human grasping using forward and inverse kinematics

Abstract

Technical Section
Database guided computer animation of human grasping using forward and inverse kinematics