Articulated swimming creatures

Tan, Jie; Gu, Yuting; Turk, Greg; Liu, C. Karen

doi:10.1145/2010324.1964953

Cited by 50 publications

(44 citation statements)

References 12 publications

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“…These systems are governed by different physical models, ranging from walking robots [Wampler and Popović 2009;Coros et al 2011], mechanical characters [Coros et al 2013], swimming characters [Lentine et al 2011;Tan et al 2011], birds [Wu and Popović 2003;Ju et al 2013], and gliders [Umetani et al 2014]. Particularly, for the sake of efficiently generating artist-desired animations, a large variety of optimization algorithms have been developed in the effort to edit and control the dynamics of passive rigid bodies, including [Popović et al 2000], [Twigg and James 2008], and [Jain and Liu 2009], to name a few.…”

Section: Related Workmentioning

confidence: 99%

Computational multicopter design

Du¹,

Schulz²,

Zhu³

et al. 2016

ACM Trans. Graph.

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Figure 1: We provide an interactive system for users to design, optimize and fabricate multicopters. We explore the design space to allow multicopter design with free-form geometry and nonstandard motor positions and directions. Based on user-specified metrics, our system optimizes the copter geometry and suggests a valid controller. Left: a multicopter example with free-form geometry, various motor heights and different propeller sizes. Middle: a pentacopter with optimized motor positions and orientations, allowing 30% increase in payload. Right: a classic hexacopter with three pairs of coaxial propellers. AbstractWe present an interactive system for computational design, optimization, and fabrication of multicopters. Our computational approach allows non-experts to design, explore, and evaluate a wide range of different multicopters. We provide users with an intuitive interface for assembling a multicopter from a collection of components (e.g., propellers, motors, and carbon fiber rods). Our algorithm interactively optimizes shape and controller parameters of the current design to ensure its proper operation. In addition, we allow incorporating a variety of other metrics (such as payload, battery usage, size, and cost) into the design process and exploring tradeoffs between them. We show the efficacy of our method and system by designing, optimizing, fabricating, and operating multicopters with complex geometries and propeller configurations. We also demonstrate the ability of our optimization algorithm to improve the multicopter performance under different metrics.

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Section: Related Workmentioning

confidence: 99%

Computational multicopter design

Du¹,

Schulz²,

Zhu³

et al. 2016

ACM Trans. Graph.

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“…The approach presented in [44] creates a realistic swimming behavior for an articulated creature with a learning process through offline optimization for appropriate maneuvers.…”

Section: Learning-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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“…Not only is it substantially harder to acquire motion data for animals than for humans, but there is also the more fundamental problem that acquiring motion data for extinct or fictional animals is impossible even in principle. Thus while some techniques have been developed to successfully synthesize animal locomotion, most existing approaches either rely on motion data or artist interaction in specifying the style of the motions [Coros et al 2011;Nunes et al 2012], apply only to non-legged locomotion [Sims 1994;Wu and Popović 2003;Tan et al 2011], or do not focus on generating realistic motions [Fang and Pollard 2003;Wampler and Popović 2009;Mordatch et al 2012].…”

Section: Related Workmentioning

confidence: 99%

Generalizing locomotion style to new animals with inverse optimal regression

Wampler

Popović

2014

ACM Trans. Graph.

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We present a technique for analyzing a set of animal gaits to predict the gait of a new animal from its shape alone. This method works on a wide range of bipeds and quadrupeds, and adapts the motion style to the size and shape of the animal. We achieve this by combining inverse optimization with sparse data interpolation. Starting with a set of reference walking gaits extracted from sagittal plane video footage, we first use inverse optimization to learn physically motivated parameters describing the style of each of these gaits. Given a new animal, we estimate the parameters describing its gait with sparse data interpolation, then solve a forward optimization problem to synthesize the final gait. To improve the realism of the results, we introduce a novel algorithm called joint inverse optimization which learns coherent patterns in motion style from a database of example animal-gait pairs. We quantify the predictive performance of our model by comparing its synthesized gaits to ground truth walking motions for a range of different animals. We also apply our method to the prediction of gaits for dinosaurs and other extinct creatures.

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Articulated swimming creatures

Cited by 50 publications

References 12 publications

Computational multicopter design

Computational multicopter design

Skill learning based catching motion control

Generalizing locomotion style to new animals with inverse optimal regression

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