omputational climbing for physics-based characterssandre17/climbing/Climbing_poster.pdf · In...
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Introduction [1] Tonneau S., Pettré J., Multon F.: Task Efficient Contact Configurations for Arbitrary Virtual Creatures. In Proceedings of Graphics Interface 2014, pp. 9-16. [2] Tan J., Liu C. K., Turk G.: Stable Proportional- Derivative Controllers. IEEE Computer Graphics and Applications, 31(4):34–44, July 2011. We present our ongoing work to create realistic climbing animations for articulated 3D characters. Our goal is to automatically synthesize physically plausible motions for highly detailed models. Specifically, for characters with attached hands, giving them true grasping capabilities. Our method chooses limb configurations that are suitable for climbing, favoring postures with the ability to effectively exchange forces between the character and its environment. Grasping postures are learned that support the character as it climbs in a dynamic environment. And a novel linear feedback controller is used to adjust the grip strength in order to deal with force variations at the wrist. The physics-based character model has 83 degrees of freedom (DOF), including 21 DOF for each hand, and a mass distribution similar to that of a real human. The RTQL8 physics engine is used for real-time simulation. Discussion & Future Work The preliminary results shown here are encouraging. we are confident that realistic climbing animations for high fidelity character models may be synthesized with our approach. Aside from an offline optimization step, little computational overhead is required, making it suitable for games and other real-time applications. Currently, we are investigating methods A heuristic based on the force transmission ratio is used to determine whether any limbs should be repositioned. The FTR is calculated by mapping the potential joint torques to a set of potential forces at the end effector, visualized by an ellipsoid in the above figure. If joints of an active limb are near limits, or if the FTR in the climbing direction drops below a certain threshold, a new target handhold is selected for that limb. A kinematic sampling process based on the method of Tonneau et al. [1] is used to select a reachable handhold with a skeleton configuration that best allows the character to push / pull in the required direction with the limb being retargeted. Character Model Controllers & Strategies Tim Gorm Kaas-Rasmussen Olsen 1,2 , Sheldon Andrews 1 , and Paul G. Kry 1 McGill University 1 University of Copenhagen 2 Computational climbing for physics-based characters Motion Planning Grasp Optimization Wrist Inverse Dynamics Grasp SPD Reach for handhold Close hand Pre-shape Wrist track spline Grasping Grasp SPD Advance climbing Body SPD – Non-ragdoll effects, track kinematic pose Adjust Grip Finger J T F Motion planning Limb J T F Pull/push to advance Phases Controllers States References A state machine is used to activate controllers depending on the phase of motion and state of the simulation. Various strategies are used to accomplish the climbing task. Body Limb Hand Inverse dynamics is used to re-target a limb not currently supporting the character. The end effector body (palm) is maneuvered to an orientation and position for pre- shaping and grasping the target handhold. The trajectory is stored as a Hermite spline. A virtual force is generated at each supporting limb according to the desired climbing direction . Limb joint torques are computed by = , where is the linear Jacobian of the end effector for limb . A pose tracking controller maintains a natural configuration for joints not actively used for climbing. SPD servos [2] are used here too. The grip controller provides online adjustments to the grasp by modulating contact forces at each finger. The force needed to support the character is estimated from inertial and external forces. A linear feedback rule is used to compute the force adjustments at each finger segment: = . Here, is the unit length contact normal at the finger segment and is a gain parameter. Joint torques are computed by = , where J i is the Jacobian of finger segment at the contact point. The grasp controller pre-shapes and closes the hand according to a grasping posture learned by the optimization step. Torques at finger joints are computed using stable proportional derivative (SPD) servos [2]. Example videos goo.gl/HNrrcz to perform online grasp planning, eliminating the need for a costly non-linear optimization step. We hope to make climbing a ubiquitous activity available to characters in any physics- based virtual environment. Furthermore, data collected with the sensor augmented climbing wall, shown in the figure on the left, will be used to determine other climbing strategies. An offline CMA optimization step learns grasping postures that maximize the forces exchanged due to contacts between the fingers and a candidate handhold. A database of grasping postures is learned for each handhold, and for various pulling directions. Rather than considering all 21 DOF of the hand independently, the optimization uses a synergy of hand postures captured from real climbing tasks by a ShapeHand sensor. This lowers the dimensionality of the search to 3 DOF for the reduced pose basis and 6 DOF for the position and orientation of the palm.
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Introduction
[1] Tonneau S., Pettré J., Multon F.: Task EfficientContact Configurations for Arbitrary Virtual Creatures.In Proceedings of Graphics Interface 2014, pp. 9-16.[2] Tan J., Liu C. K., Turk G.: Stable Proportional-Derivative Controllers. IEEE Computer Graphics andApplications, 31(4):34–44, July 2011.
We present our ongoing work to create realisticclimbing animations for articulated 3D characters. Ourgoal is to automatically synthesize physically plausiblemotions for highly detailed models. Specifically, forcharacters with attached hands, giving them truegrasping capabilities. Our method chooses limbconfigurations that are suitable for climbing, favoringpostures with the ability to effectively exchange forcesbetween the character and its environment. Graspingpostures are learned that support the character as itclimbs in a dynamic environment. And a novel linearfeedback controller is used to adjust the grip strengthin order to deal with force variations at the wrist.
The physics-based character model has 83 degrees offreedom (DOF), including 21 DOF for each hand, and amass distribution similar to that of a real human. TheRTQL8 physics engine is used for real-time simulation.
Discussion & Future Work
The preliminary results shown here are encouraging.we are confident that realistic climbing animations forhigh fidelity character models may be synthesizedwith our approach. Aside from an offline optimizationstep, little computational overhead is required,making it suitable for games and other real-timeapplications. Currently, we are investigating methods
A heuristic based on the force transmission ratio isused to determine whether any limbs should berepositioned. The FTR is calculated by mapping thepotential joint torques to a set of potential forces atthe end effector, visualized by an ellipsoid in theabove figure. If joints of an active limb are near limits,or if the FTR in the climbing direction drops below acertain threshold, a new target handhold is selectedfor that limb. A kinematic sampling process based onthe method of Tonneau et al. [1] is used to select areachable handhold with a skeleton configuration thatbest allows the character to push / pull in therequired direction with the limb being retargeted.
Character Model
Controllers & Strategies
Tim Gorm Kaas-Rasmussen Olsen1,2, Sheldon Andrews1, and Paul G. Kry1
McGill University1 University of Copenhagen2
Computational climbing for physics-based characters
Motion Planning
Grasp Optimization
Wrist Inverse Dynamics
Grasp SPD
Reach for handhold
Close handPre-shape
Wrist track spline
Grasping
Grasp SPD
Advance climbing
Body SPD – Non-ragdoll effects, track kinematic pose
Adjust Grip
Finger JTF
Motion planning
Limb JTF
Pull/push to advance
Phases
Controllers
States
References
A state machine is used to activate controllers depending on the phase ofmotion and state of the simulation. Various strategies are used to accomplishthe climbing task.
Body
Limb
Hand
Inverse dynamics is used to re-target alimb not currently supporting the character.The end effector body (palm) is maneuveredto an orientation and position for pre-shaping and grasping the target handhold.The trajectory is stored as a Hermite spline.
A virtual force is generated at each supporting limbaccording to the desired climbing direction 𝑣. Limb joint
torques are computed by 𝜏 = 𝐽𝑙𝑇 𝑣 , where 𝐽𝑙 is the linear
Jacobian of the end effector for limb 𝑙.
A pose tracking controllermaintains a natural configurationfor joints not actively used forclimbing. SPD servos [2] are usedhere too.
The grip controller provides online adjustments to the grasp bymodulating contact forces at each finger. The force needed tosupport the character 𝑓𝑠 is estimated from inertial and externalforces. A linear feedback rule is used to compute the forceadjustments at each finger segment:
𝑓𝑖 = 𝛼 𝑛 𝑛𝑇𝑓𝑠 .Here, 𝑛 is the unit length contact normal at the finger segment and
𝛼 is a gain parameter. Joint torques are computed by 𝜏 = 𝐽𝑖𝑇𝑓𝑖,
where Ji is the Jacobian of finger segment 𝑖 at the contact point.
The grasp controller pre-shapes and closes the hand according to agrasping posture learned by the optimization step. Torques at fingerjoints are computed using stable proportional derivative (SPD) servos [2].
Example videos goo.gl/HNrrcz
to perform online graspplanning, eliminating theneed for a costly non-linearoptimization step. We hopeto make climbing aubiquitous activity availableto characters in any physics-based virtual environment.Furthermore, data collectedwith the sensor augmentedclimbing wall, shown in thefigure on the left, will beused to determine otherclimbing strategies.
An offline CMA optimization step learns graspingpostures that maximize the forces exchanged due tocontacts between the fingers and a candidatehandhold. A database of grasping postures is learnedfor each handhold, and for various pulling directions.Rather than considering all 21 DOF of the handindependently, the optimization uses a synergy ofhand postures captured from real climbing tasks by aShapeHand sensor. This lowers the dimensionality ofthe search to 3 DOF for the reduced pose basis and 6DOF for the position and orientation of the palm.
