Boulic, R.

Mas, R.

Thalmann, D.

Infoscience - École polytechnique fédérale de Lausanne

Extended Grasping Behavior for Autonomous Human AgentsRamon Mas1, Ronan Boulic2 , Daniel Thalmann21 Department of Mathematics and Computer Science, Balearic Islands University,  Spain (ramon@anim.uib.es)2 Computer Graphics Laboratory., Swiss Federal Institute of Technology Switzerland (boulic@lig.di.epfl.ch)AbstractIn this paper we present an efficient technique allowing distantgrasp behaviors while preserving the static balance. This featuregreatly  increases the autonomy of virtual  humans.1. IntroductionOur goal is to provide an integrated reaching and graspingbehaviors for a virtual human agent. In most cases, grasping aclose  object only involves the motion of one or both arms oftencomplemented with the independent motion of the head to looktowards the goal [RG91][MT94].  However the reach of distantobjects is a much more complex task as human beings can extendtheir field of action by various means.  Let us imagine the processof grasping a relatively low object (Fig. 1a). To avoid a loss ofbalance the body  posture is constrained so as to keep the verticalprojection of the center of mass inside the base of support (Fig.1b). However the object can be even more distant and additionalsupporting area has to be used.(Fig. 1c). In the case of Fig. 1c wecan reach the target with the left hand while leaning on the boxwith the right hand  (Fig. 1d). The present approach solves thisclass of problems on a human body model  with 95 degrees offreedom including the hands mobilbities [MT94].a b c dFigure 1. Reaching a close object (a,b)and a distant target (c,d)2. The Grasp Decision MechanismOur approach decomposes into two phases. In the first phase agrasp is selected and the associated hand position defines thedesired hand goal. In the second phase the hand goal is used as anadditional constraint for our Inverse Kinetics process [BMT95].We consider the use of basic primitives first proposed by[TBK87] as being the most adequate to help in the grasp choice.Each object has a set of associated volumic primitives where eachprimitive characterizes a part of the object that can be grasped.We have adapted a grasp taxonomy [CH89] which provides aclassification depending on the degree of precision of the task andon the geometrical characteristics of the objects (Fig. 2).GraspSphere TripodPrecisionCompact LongCircular PrismaticSmallPinchPowerClamping No ClampingNon Prehensile PrehensileHookCompactLong ThinCircularPrismaticLateralSphereDiskSmall DiameterLarge DiameterFigure 2 : The grasp taxonomy3. The Reaching StrategyThe reaching procedure has to bring the hand in a positionallowing the grasping of the object. The main factor influencingthis procedure is the location of the object relative to the body.Three reach  areas are considered : 1) the arm reach area, 2) theDefault Body  Reach  Area with support as in Fig. 1a (in shortDBRA), 3) the Extended Body  Reach Area with additionalsupport (in short EBRA).To determine whether the object belongs to the arm reach area ismade with a simple test :  the distance from the hand to the targetis compared to the length of the arm (from the shoulder to thehand). Testing whether the object belongs to the DBRA isdifficult to evaluate without a priori knowledge. Moreover, wealso have to consider the additional constraint imposed by thebalance control requirement and the existence of joint limits.  Forthis reason, we pre-compute the DBRA while standing withbalance on both  feet .If the object does not belong to the DBRA, new supportcombinations have to be examined.  The starting point is the setof all the possible combinations of the four basic effectors (feetand hands). One of them is always considered as the root for thepropagation of the motion, so it has to remain fixed. For this taskwe give priority to lower limbs, as they are stronger than theupper limbs to sustain  the body.  Furthermore, the hand used forthe grasp cannot be used as a supporting site. So we have onlytwo end effecors to test for selecting the additional support.Distance heuristics are used to create an ordered list of pairs(supporting site, effector) with decreasing probability of fulfillingthe goal.  The best candidate finally determines the motioncontrol structure as illustrated in section 5.4. Inverse KineticsWe have proposed a new control technique which takes intoaccount the mass distribution of the body to control precisely theposition of the Center of Mass; for this reason we call it InverseKinetics [BMT95]. We can guarantee the static balance of thebody during the reach process by ensuring that the position of thecenter of mass lies inside the sustentation polygon. When thebody is supported by more than one site, the multiple supportscase, we face two problems : 1) How to ensure the geometricconstraints of additional support locations ? 2) How to ensure thecontinuity of the center of mass position control relatively tomodifications of the support distribution ?The first problem is a standard Inverse Kinematics problem wherethe additional supports are considered as end effectors. Thesecond problem requires a center of mass position controldepending on the support contribution of each site. Our solutionis described in [BMT95].The reaching behavior emerges from the combined performanceof the hand attraction to the target object, with InverseKinematics, while the center of mass is retained inside thepolygon of sustentation, with Inverse Kinetics. The combinationeven allow an additional optimization to take place as describedin  [BMT95].5. Simulation ResultsFigure 3 shows  grasp postures for different positions of the targetobject located inside the reachable workspace. In all the cases thebody balance is ensured by projecting in the sustentation polygon.a b cFigure 3 : variousreach behaviors ofclose objectsensuring the balanceFigure 4 illustrates the animation for grasping a small sphere outof the reachable workspace. First, the grasp decision processselects a tripod as the more convenient mode using Fig. 2taxonomy. Second, the left hand is activated as the one fulfillingthe smallest distance requirement. As the distance from the handto the target is larger than the arm length, the full body has to beinvolved. Third, the DBRA voxel data structure is checked andthe object is classified outside that space. Fourth, a search on thelist of plausible sites within the workspace returns the upper sideof the box. Given its location and height it is more suitable for theauxiliary hand than for the leg. Fifth, the additional support centerpoint is computed and assigned as a target to the the right hand.The left leg is allowed to move freely. So  the initial state of themotion control  is :  1) the left hand has to grasp the sphere usinga tripod mode.  2) the right hand has to be placed on theadditional support. 3) The balance has the highest priority.From the default position the human agent moves both handstowards their respective goal while the center of mass isconstrained to project in the base of support defined by the rightfoot (see the vertical line in Fig. 4a and 4b). When the right handreaches its target (figure 4c) the base of support is enlarged (nowit is defined by the projection on the ground of the line joining theright leg and the right hand). Figures 4b, 4c and 4d show clearly acontinuous displacement of the center of mass on that line untilthe object is reached (use the right foot as a visual reference).When the left hand is at a short distance from the sphere the handstarts closing till the collisions of the fingers with the objects aredetected.The simulation of the sequence takes about 15 seconds on aR4000 computer which is to be compared with the average 6seconds for a real person to do the same task.a bc dFigure 4: Grasping a distant sphere6. ConclusionWe have presented an integrated reaching and grasping behaviorfor a virtual human  agent. We plan to complete this model withthe information of the weight and volume of the grasped objectwhich are very significant to determine the succes of a grasp task.7. AcknowledgmentThe research was partly supported by the Swiss NationalFoundation for Scientific Research.8. References[BMT95] Boulic, R., Mas, R., Thalmann, D. (1995) “PositionControl of the Center of Mass for Articulated Figures inMultiple Support”, Eurographics Workshop on ComputerAnimation and Simulation 1995, D. Terzopoulos & D.Thalmann Eds., pp 130-143, ISBN  3-211-82738-2, SpringerVerlag Wien[CH89] Cutkosky, M. R., Howe, R. D. (1989). Human GraspChoice and Robotic Grasp Analysis. In S. T. Venkataraman, &T. Iberall (Ed.), in “Dextrous Robot Hands” Springer-Verlag.[MT94] Mas, R., Thalmann, D. (1994). A Hand Control andAutomatic Grasping System. Eurographics’94, ComputerGraphics Forum, 13(3), pp 167-177.[RG91] Rijpkema, H., Girard, M. (1991). Computer Animation ofKnowledge-Based Human Grasping. Computer Graphics, LasVegas: ACM SIGGRAPH, pp 339-348.[TBK87] Tomovic, R., Bekey, G. A., Karplus, W. J. (1987). AStrategy for Grasp Synthesis with Multifingered Robot Hands.Proceedings IEEE on Robotics and Automation, pp 83-89.

Extended Grasping Behavior for Autonomous, Human Agents

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Extended Grasping Behavior for Autonomous, Human Agents

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