Rijpkema, H. and Girard, M. (1991). Computer animation of knowledge-based human grasping. Computer Graphics (SIGGRAPH '91 Proceedings), 25(4), Robertson, B. (1995). Toy story: A triumph of animation. Computer Graphics World,18 Mathematics,8 serial-link robot manipulators. Robotica, 12, 309--322.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....virtual humans. 1. Introduction Our goal is to provide an integrated reaching and grasping behaviors for a virtual human agent. In most cases, grasping a close object only involves the motion of one or both arms often complemented with the independent motion of the head to look towards the goal [RG91][MT94] However the reach of distant objects is a much more complex task as human beings can extend their field of action by various means. Let us imagine the process of grasping a relatively low object (Fig. 1a) To avoid a loss of balance the body posture is constrained so as to keep the ....

Rijpkema, H., Girard, M. (1991). Computer Animation of Knowledge-Based Human Grasping. Computer Graphics, Las Vegas: ACM SIGGRAPH, pp 339-348.

....specific functional models controlled by a few higher level parameters. The examples of walking and grasping are determined respectively by speed and hand closure. In fact, the proposed models are driven through both joint and cartesian space kinematic control rather than pure joint space control [10,12,13,14,15,16]. Finally, the main conterpart of this great freedom in the design process is usually a lack of physical completeness in the resulting motion. Conversely, dynamic control of motion suffers from the difficulty of predicting the motion resulting from direct joint torque specification. Applications ....

H. Rijpkema and M. Girard, "Computer animation of knowledge-based human grasping", SIGGRAPH '91 Computer Graphics , 25(4), pp. 339-348 (1991).

.... in film, the gross behavior of human motion is the key element, and subtle features like the hand can remain simplistic [Badle99] The main body of work in computer animation of the human hand has revolved around the ability of an animated character to grasp objects effectively and convincingly [Rijpk91]. However, the act of grasping does not encompass the full range of expressiveness that a human hand can achieve. This work has limited usefulness for other applications, such as depicting ASL, because the hand is almost always in an open configuration necessary to encompass the object, see Figure ....

....in the hand. We have already noted the correlation between the spread of the fingers and a small twist about their z axes. In addition, there is a far more noticeable correlation between the DIP and PIP joints in the fingers for hooking actions. As has been observed in previous works [Lands58][Rijpk91], the tendons that bend the DIP also bend the PIP. In most hooking actions of the fingers, such as during grasping, the DIP bend is approximately 2 3 that of the PIP [Rijpk91] We isolate this rotational correlation as an independent action called the hook of the finger. There is also independent ....

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Rijpkema, H, Girard, M: Computer Animation of Knowledge-Based Human Grasping, Computer Graphics, Vol 25, No 4, July 1991, pp 339-348.

....methodology. Keywords: grasping, virtual human, digital glove 1.Introduction With the advents of synthetic actors in computer animation, study of human grasping has become a key issue in this field. The common used method is a knowledge based approach for grasp selection and motion planning [RG91]. It can be completed with a proximity sensor model [EB85] or a sensor actuator model [vdPF93] for the precise position control of the fingers around the object [MT94] This method results in an automatic grasping procedure. Moreover, due to the 3D interactive tools widely available today, we ....

Rijpkema H and Girard M. (1991) Computer animation of knowledge-based human grasping, Proceedings of Siggraph'91, pp.339-348.

....further refinements to the position of the palm. A grasping system should therefore incorporate both the serial and parallel aspects of human prehension. 2. 2 Previous Approaches to Simulated Grasping There is some previous work in the graphic simulation of automatic human prehension, notably [RG91, MTN88, MTT91] and [KKKL94] Rijpkema and Girard present an inverse kinematics approach to control of the fingers and thumb. This computationally expensive approach denies the 5 close connection between sensory input and the resultant grasp. Furthermore, they concentrate primarily on pad grasps, ....

Hans Rijpkema and Michael Girard. Computer animation of knowledge-based human grasping. In Thomas W. Sederberg, editor, Computer Graphics (SIGGRAPH '91 Proceedings) , volume 25, pages 339--348, July 1991.

....at the cost of a redundant parameter. The total hand pose is described by a 28 dimensional state vector. The thumb is the most difficult digit to model, due to its great dexterity and intricate kinematics. We currently employ the thumb model used in Rijpkema and Girard s grasp modeling system [12] (see Fig. 1) They were able to obtain realistic animations of human grasps using a five DOF model. DH parameters for the first author s right hand, used in the experiments, can be found in [11] Real fingers deviate from our modeling assumptions in three ways. First, most fingers deviate ....

H. Rijpkema and M. Girard. Computer animation of knowledge-based human grasping. Computer Graphics, 25(4):339--348, 1991.

....motion while perturbing a single static grasp is introduced in Perlin, et al. 120] A complete task may comprise several homogeneous manipulations. Perlin, et al. 120] propose a structured and hierarchi 1. This has been variously referred to as reaching ( 4] 9] 149] and target approach ([129], 149] However, these terms can be easily confused with the hand transportation component of this phase Jeannerod [62] for example, uses the terms reaching and transportation interchangeably. 19 cal approach to autonomous manipulation, specifically for the Utah MIT hand. The scheme ....

H. Rijpkema and M. Girard, "Computer animation of knowledge-based human grasping," Computer Graphics, Vol. 25, No. 4, 1991, pp. 339-348.

.... of animation contexts [2] Reducing the number of degrees of freedom under direct animator control in the generation of hand animations is also motivated by the fact that the mechanical structure of the hand introduces constraints that reduce the ability to control hand joints independently (see [6] for a task level computer animation application) In our case, we use the following cost function: Cost(#) # i #P i tip (#) P i goal # 2 # j barrier(# j ) # j 1 # 2 j ## j # j # 2 (3) where: # = # 1 , # 2 , # n is the joint value vector to ....

Hans Rijpkema and Michael Girard. Computer animation of knowledge-based human grasping. Computer Graphics, 25(4):339--348, July 1991.

....the thumb. Due to its great dexterity and intricate kinematics, it is very difficult to model the thumb. Regh and Kanada used 5 degrees offreedom for the thumb (an additional degree of freedom represents the yaw movement on the MCP joint of the thumb shown in Figure 6) in DigitEyes, as used by Rijpkema Girard (1991) for the realistic animation of human grasps. View of the right hand F0. F4 represent thumb, index, middle, fourth and last finger. CMC: CarpoMetaCarpal; MCP: MetaCarpoPhalangeal; PIP: ProximalInterPhalangeal; DIP: DistalInterPhalangeal; IP:InterPhalangeal; Local coordinate system for ....

Rijpkema, H. and Girard, M. (1991). Computer animation of knowledge-based human grasping, Computer Graphics 25(4): 339-348.

....as the formation of wrinkles could be rendered by setting tangent end conditions at the moving contour instead of curvature constraints. We have build a twenty degrees of freedom articulated hand from the model shown in Figure 10. We created four joints per finger following the same taxonomy as [15]. Joints are created fully interactively by selecting the five items. Figure 12 shows the model with the twenty articulations as seen from the interface. User may select each joint and set its rotation angle. 10 CONCLUSION In this paper we have presented a physically based modeling system that ....

H. Rijpkema and M. Girard. Computer animation of knowledge-based human grasping. In Computer Graphics (SIGGRAPH'91), pages 339--347, July 1991.

....dexterity and intricate kinematics, it is very difficult to model the thumb. Regh and Kanada [13] used 5 DOFs for the thumb in DigitEyes (the additional DOF represents the yaw movement on the MCP (Meta Carpo Phalangeal) joint of the thumb shown in Figure 4) as was used by Rijpkema and Girard [17] for the realistic animation of human grasps. View of the right hand F0. F4 represent thumb, index, middle, fourth and last finger. CMC: CarpoMetaCarpal; MCP: MetaCarpoPhalangeal; PIP: ProximalInterPhalangeal; DIP: DistalInterPhalangeal; IP:InterPhalangeal; Local coordinate system for ....

H. Rijpkema and M. Girard, "Computer animation of knowledge-based human grasping", Computer Graphics, Vol. 25(4), pp. 339-348, 1991.

....frame is located after the first transform, and so the kinematic to shape frame transform is just Rot z; i . The thumb is the most difficult digit to model, due to its great dexterity and intricate kinematics. We currently employ the thumb model used in Rijpkema and Girard s grasp modeling system [13] (see Fig. 2) They were able to obtain realistic animations of human grasps using a five DOF model. The DH parameters for the first author s right hand, used in the experiments, can be found in Table 1. Real fingers deviate from our modeling assumptions in three ways. First, most fingers deviate ....

H. Rijpkema and M. Girard. Computer animation of knowledge-based human grasping. Computer Graphics, 25(4):339--348, 1991.

....matrix computation. For the moment only fingertips are candidates for hand VE interaction. The synthesis of human hand motion and grasping of CH Normal VH VO Intention Tangential (slip) Figure 2: Virtual hand model arbitrary shaped objects is a very complex problem. Rijkpkema and Girard [22] proposed a hi level control to perform these actions. However this kind of control is knowledge based and somehow autonomous. In our case grasping is performed according to operator hand position and orientation and fingertip penetration within the grasped object by means of a mathematical model ....

H. Rijpkema, M. Girard, "Computer Animation of Knowledge-Based Human Grasping", ACM Journal of Computer Graphics, Vol. 25, No. 4, pp. 339-348, 1991.

....parameters (such as grasp site, grip type or approachvector) must be supplied. Currently a knowledge base is used to determine from the OSR motion, the object type and the intention of the task action these additional parameters. This is similar to approaches taken by [IJLZ88, TBK87, RG91] The fourth stage (Feasibility Checker) involves checking dependencies between the agent resources and object attributes. Each OSR motion includes a predicate which specifies those pairs of resources and attributes to check. For example, the OSR might check whether the agent s hand is large ....

Hans Rijpkema and Michael Girard. Computer animation of knowledge-based human grasping. In ACM: Computer Graphics, pages 339--348, July 1991.

....(see Figure 1) The first hand model of interest is an angle based hand model, which consists of 26 parameters corresponding to 20 DOFs of joints (4 DOFs for each finger and thumb) and 6 DOFs of the position and orientation of the hand. This hand model is popular in human hand modeling, like in [Rijpkema Girard, 1991]. The angle based hand model has enough descriptive power to satisfy the need of hand modeling in virtual environments. However, it does not provide any further convenient features to support any approach of utilizing gestures. 2.2. A Point based Hand Model We have observed that pointing ....

....(DIP) joints. Only two of them are major factors controlling the configuration of a finger: the bending angle of the MP joint (the proximal bend, PB) and the angle of the PIP joint (the middle bend, MB) The angle of the DIP joint is approximately two third of the angle of the PIP joint [Rijpkema Girard, 1991]) Although the values of PB range roughly from to =2, we found that only three values occur in Stokoe s primes and sub primes: 2, and their in between (near 3 =4) For the MB, there are only two values: and =2. Therefore, we have extracted 2 3 = 6 configurations for each finger ....

Rijpkema, H. & Girard, M. [1991] "Computer animation of knowledge-based human grasping", Proc. ACM SIGGRAPH'91 , pp. 339-348.

....Par exemple, l angle de flexion extension des premieres phalanges des quatre doigts est compris entre 110 o et 15 o . Les contraintes dynamiques representent les relations entre les degres de liberte des articulations des doigts. Pour les doigts autres que le pouce, ces contraintes sont [Kush95, Lee95, Millar96, Teo94, Buchholz92, Rijpkema91] : la relation entre les flexions de la phalangette et de la phalangine : # IPD f e = 2 3 # IPP f e (1) l interdependance entre la flexion extension et l abduction adduction de chaque phalange. En e#et, plus l angle de flexion extension est grand, plus l abduction ou l adduction des ....

....de l alphabet de la langue des signes (A, D, F respectivement. Apres ajustement, le modele articule est destine a etre anime pour reproduire les mouvements de la main dans des sequences video. Les methodes d animation procedurale et declarative [Teo94] peuvent utiliser la dynamique inverse [Rijpkema91], l analyse par elements finis ou l interpolation de plans [Gourret89] Le modele de la main ainsi que les animations ont ete developpes en VRML sur station Silicon Graphics au moyen du logiciel Open Inventor. 6 Ajustement du modele Le modele generique 3D ne correspond pas en general a la ....

H. RIJPKEMA and M. GIRARD, "Computer Animation of Knowledge-Based Human Grasping", Computer Graphics, Vol. 25, no. 4, 1991, p. 339-348.

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Rijpkema, H. and Girard, M. (1991). Computer animation of knowledge-based human grasping. Computer Graphics (SIGGRAPH '91 Proceedings), 25(4), Robertson, B. (1995). Toy story: A triumph of animation. Computer Graphics World,18 Mathematics,8 serial-link robot manipulators. Robotica, 12, 309--322.

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Hans Rijpkema, and Michael Girard. Computer animation of knowledge-based human grasping. In Computer Graphics (SIGGRAPH '91 Proceedings), Thomas W. Sederberg, Ed., vol. 25, pages 339--348, July 1991. 215

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H. Rijpkema and M. Girard. Computer Animation of Knowledge-Based Human Grasping. ACM Computer Graphics (Proc. of SIGGRAPH 91), 339--347, 1991.

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H. Rijpkema and M. Girard. Computer Animation of Knowledge-Based Human Grasping. In Thomas W. Sederberg, editor, Computer Graphics (SIGGRAPH '91 Conf. Proc.), volume 25, pages 339--348. ACM SIGGRAPH, 1991.

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Hans Rijpkema and Michael Girard. Computer animation of knowledge-based human grasping. Computer Graphics, 25(4):339--348, July 1991.

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