MovementControl Metho ds for Complex Dynamically Simulated Agents Adonis Dances the Macarena Ma ja J Mataric Victor B Zordan Zachary Mason Computer Science Department College of Computing Computer Science Department University of Southern California Georgia Institute of Technology Brandeis University Los Angeles CA Atlanta GA Waltham MA tel tel tel matariccsuscedu victorccgatechedu zmasoncsbrandeisedu area in realistic physicallybased animation where Abstract the control of dynamic simulations of human charac We describ e and compare two implemented controllers ters involving realistic physical mo dels matches the for Adonis a physically simulated humanoid torso complexity of the rob otics problem Pai Ho d one based on jointspace torques and the other on gins Wo oten Brogan OBrien Van de Panne convergent forceelds applied to the hands The two Lamouret come from dierent application domains the former Weintro duce a novel approach to manipulator is a common approach in manipulator rob otics and control based on mo dels from neuroscience which graphics while the latter is inspired by biological limb employconvergent forceelds at the endp oints of control Both avoid explicit inverse kinematic calcu a manipulator We explore the feasibilityofsucha lations found in standard Cartesian control trading mo del for complex animation and agent control in generality of motion for programming eciencyThe general and discuss how it can b e generalized to two approaches are compared on a common sequen highlevel motor tasks and incorp orated into a gen tial task the familiar dance Macarena and evalu eral control framework F urthermore we compare ated based on ease of generating new b ehaviors exi the metho dology to a standard rob otics approach bility and naturalness of movement w e also compare which has also b een adopted in animation employ them against human p erformance on the same task ing jointspace control with torque actuators Both Finallywe discuss the tradeos and presentamore approaches are app ealing b ecause they avoid explicit general framework for addressing complex motor con computation of inverse kinematics IK found in stan trol of simulated agents dard Cartesian control The inputs of each controller are used explicitly as either p ositions or orientations without IK solvers converting the input data How Intro duction ever this presents a tradeo b etween generalityof Control of humanoid agents dynamically simulated motion and programming eciencyTo compare the or physical is an extremely dicult problem due to two approaches we implemented them on a common the high dimensionality of the control space ie the motor task a continuous sequence of smo oth move many degrees of freedom and the redundancy of the ments For the purp ose of evaluation a well known system In rob otics standard metho ds havebeende goaldriven sequence was chosen the p opular dance velop ed for simpler manipulators and have b een grad Macarena The dance presents a nontrivial well ually scaled up to more complex arms Paul dened task that can b e precisely sp ecied and eval Brady Hollerbach Johnson LozanoPerez Mason uated b oth relative to the quantitative sp ecication and recently to physical humanlikearmsSchaal and to qualitativehuman p erformance Williamson The problem of anthrop o morphic control has also found a new application Background Control in Rob otics and Computer Animation Computer animation and rob otics are two primary areas of researchinto motion for articial agents D animated character motion has traditionally b een cre Our work is inspired by a sp ecic principle derived ated by hand through a timeconsuming pro cess Re from evidence in neuroscience MussaIvaldi Giszter centlyphysical mo deling has b een used to generate Giszter MussaIvaldi Bizzi and re motion by minimizing usersp ecied constraints while lated work on spinalized frogs and rats suggest the ex allowing the mo del constraints to add physical real istence of forceeld motor primitives that converge ism Witkin Kass pursue physical mo deling to single equilibrium p oints and pro duce highlevel through such a constraintbased approach bycho os behaviors suchasreaching and wiping When a par ing start and end conditions they generate anticipa ticular eld is activated the frogs leg executes a b e tion and determination in the action Cohen havior and comes to rest at a p osition that corre extended this approach with higher DOF systems and sp onds to the equilibrium p oint when two or more more complex constraints Ngo Marks in elds are activated either a linear sup erp osition of tro duce a constraint approach of creating b ehaviors the elds of tested cases or a winnertakeall automatically using genetic algorithms resp onse of remaining cases results Mussa Dynamic simulation has b een used to generate Ivaldi Giszter Bizzi This suggests an el graphical motion by applying dynamics to physically egant organizational principle for motor control in based mo dels and using forward integration Simu tire b ehaviors are co ded with lowlevel force whichen lation ensures physically plausible motion by enforc elds and maybecombined into higherlevel more ing the laws of physics Pai simulates walk complex b ehaviors The idea of supplying an agent ing gaits drawing strongly from rob otics work His with a collection of basis behaviors or primitives rep torso and legs use a controller based on highlevel resenting forceelds and combining those into a gen timevarying constraints Handtuned control of sim eral rep ertoire for complex motion is very app ealing ulations has b een applied successfully to more com Our previous work Mataric Mataric in plex systems such as full articulated human gures spired by the same biological results has already suc Raib ert Ho dgins demonstrate rigid b o dy cessfully applied the idea of basis behaviors to control dynamic simulations of legged creatures Their hand of planar mobile agentsrob ots This pap er extends tuned controllers consist of state machines that cy the notion to agents with more DOFs cle through rulebased constraints to p erform dier Another inspiration comes from psychophysical data ent gaits Ho dgins et al extend this work describing what p eople xate on when observing hu to human characters suggesting a to olb ox of tech man movement MataricPomplun demon niques for controlling articulated humanlike systems strate that when presented with videos of human n to generate athletic b ehaviors such asDrunning ger hand and arm movements observers fo cus on diving and bicycling Van de Panne Lamouret the hand yet when asked to imitate the movements use searchtechniques to nd balancing con sub jects are able to reconstruct complete tra jecto trollers for humanlikecharacter lo comotion aiming ries even for unnatural movements involving mul at more automatic control of simulated agents tiple DOFs in spite of having attended to the end In rob otics manipulator control has b een largely point This could suggest some form of internal mo d addressed for p ointtop ointreaching typically by els of complete b ehaviors or primitives for movement sp ecifying Cartesian D goals and explicitly solving which eectively encapsulate the details of lowlevel the IK for the manipulators joint angles Paul control Given an appropriately designed motor con Brady et al Various neural network approaches troller tasks could b e sp ecied largely by endp oint to learning IK for simple manipulators havebeenex p ositions and a few additional constraints and the plored and more sophisticated learning metho ds for controller could generate the appropriate corresp ond dynamic tasks and higher DOF systems are b eing ing p ostures and tra jectories The forceeld approach develop ed Atkeson Schaal Atkeson we describ e is a small step toward such an approach The work most similar to the forceeld approachwe To compare it with an alternative we implemented describ e was p erformed by Williamson who b oth on a common testb ed describ ed next presented a controller for a DOF rob ot arm based on the same biological evidence we describ e next It consists of four b ehaviors three reaching and one The Dynamic Anthrop omorphic Simulation Adonis resting p osture intermediate targets are achieved by linear interp olation Adonis is a rigidb o dy simulation of a human torso with static graphical legs Figure consisting of eight rigid links connected with revolute joints of one Biological Inspiration and three DOFs totaling DOFs The dynamic The exibility and eciency of biological motion pro mo del for Adonis was created by metho ds describ ed vides a desirable mo del for complex agentcontrol meaningful instructions as opp osed to fully describ ing the tasks at the simulation level The Macarena sp ecication omitting the hip and wholeb o dy sub tasks at the end is given b elow Extend Right Arm straight out palm down Extend Left Arm straight out palm down Rotate Right Hand palm up Rotate Left Hand palm up TouchRight Hand to top of your left shoulder Touch Left Hand to top of your right shoulder TouchRight Hand to the backofyour head Touch Left Hand to the backofyour head TouchRight Hand to the left side of your ribs Touch Left Hand to the right side of your ribs Move Right Hand to your right hip Move Left Hand to your left hip No taskplanning was necessary b ecause the se quence is provided by the dance sp ecication How Figure The Adonis dynamic simulation testb ed ever the individual subtasks are not sp ecied in the same frame of reference The rst four