Optimizing walking controllers

Wang, Jack M.; Fleet, David J.; Hertzmann, Aaron

doi:10.1145/1661412.1618514

Cited by 62 publications

(79 citation statements)

References 34 publications

(17 reference statements)

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“…Despite this, it has been investigated across numerous tasks, such as balancing, standing, walking, etc., but no doubt the great amount of work has been released in walking and dynamic balance strategies (e.g. [16], [17], [18], [19]). …”

Section: Physics-based Approachesmentioning

confidence: 99%

Skill learning based catching motion control

Cimen

Kavafoglu

et al. 2015

Computer Animation & Virtual

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In real world, it is crucial to learn biomechanical strategies that prepare the body in kinematics and kinetics terms during the interception tasks, such as kicking, throwing and catching. Based on this, we presents a real-time physics-based approach that generate natural and physically plausible motions for a highly complex task-ball catching. We showed that ball catching behavior as many other complex tasks, can be achieved with the proper combination of rather simple motor skills, such as standing, walking, reaching. Since learned biomechanical strategies can increase the conscious in motor control, we concerned several issues that needs to be planned. Among them, we intensively focus on the concept of timing. The character learns some policies to know how and when to react by using reinforcement learning in order to use time accurately. We demonstrate the effectiveness of our method by presenting some of the catching animation results executed in different catching strategies.In each simulation, the balls were projected randomly, but within a interval of limits, in order to obtain different arrival flight time and height conditions.

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Section: Physics-based Approachesmentioning

confidence: 99%

Skill learning based catching motion control

Cimen

Kavafoglu

et al. 2015

Computer Animation & Virtual

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“…Their controllers benefit from a learned control policy in that they are made more robust by interpolating optimal control parameters from nearby states. Similarly, Wang et al [14] perform optimization for walking controllers that anticipate perturbation.…”

Section: Related Workmentioning

confidence: 99%

Goal directed multi-finger manipulation: Control policies and analysis

Andrews

Kry

2013

Computers & Graphics

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We present a method for one-handed, task-based manipulation of objects. Our approach uses a mid-level, multi-phase approach to organize the problem into three phases. This provides an appropriate control strategy for each phase and results in cyclic finger motions that, together, accomplish the task. The exact trajectory of the object is never specified since the goal is defined by the final orientation and position of the object. All motion is physically based and guided by a control policy that is learned through a series of offline simulations. We also discuss practical considerations for our learning method. Variations in the synthesized motions are possible by tuning a scalarized multi-objective optimization. We demonstrate our method with two manipulation tasks, discussing the performance and limitations. Additionally, we provide an analysis of the robustness of the low-level controllers used by our framework.

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“…Physics-based animation research that explicitly deals with injury is limited, although several papers demonstrate its potential in that field [7,8]. Parameter optimization (one possible use for our injury measure) has been shown to lead to interesting behaviors [9][10][11]. For example, Wang et al [11] show how walking controllers can be made more robust by optimizing for random external perturbations.…”

Section: Related Workmentioning

confidence: 99%

Injury Assessment for Physics-Based Characters

2011

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Abstract. Determining injury levels for virtual characters is an important aspect of many games. For characters that are animated using simulated physics, it is possible assess injury levels based on physical properties, such as accelerations and forces. We have constructed a model for injury assessment that relates results from research on human injury response to parameters in physics-based animation systems. We describe a set of different normalized injury measures for individual body parts, which can be combined into a single measure for total injury. Our research includes a user study in which human observers rate the injury levels of physics-based characters falling from varying heights at different orientations. Results show that the correlation between our model output and perceived injury is stronger than the correlation between perceived injury and fall height (0.603 versus 0.466, respectively, with N = 1020 and p < 0.001).

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Optimizing walking controllers

Cited by 62 publications

References 34 publications

Skill learning based catching motion control

Skill learning based catching motion control

Goal directed multi-finger manipulation: Control policies and analysis

Injury Assessment for Physics-Based Characters

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