Trajectory Optimization for Full-Body Movements with Complex Contacts

Borno, Mazen Al; Lasa, Martin de; Hertzmann, Aaron

doi:10.1109/tvcg.2012.325

Cited by 106 publications

(61 citation statements)

References 57 publications

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“…These models are effectively applied to sampling-based trajectory optimization [MDLH10], linear quadratic regulator (LQR) [KH10] and differential dynamic programming (DDP) [YL10,HNJS14] to facilitate interactive performance for motion control. For derivative-free optimization, sampling-based methods were introduced to physically-based motion synthesis [WP09, LYvdP * 10, ABDLH13,LYG15]. employed for enhancing physical realism of synthesized motions [WK88, PW99, FP03, SHP04, LHP05, MTP12] .…”

Section: Related Workmentioning

confidence: 99%

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

Han

Eom

Noh

et al. 2016

Computer Graphics Forum

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In this paper, we propose an efficient data-guided method based on Model Predictive Control (MPC) to synthesize a full-body motion. Guided by a reference motion, our method repeatedly plans the full-body motion to produce an optimal control policy for predictive control while sliding the fixed-span window along the time axis. Based on this policy, the method computes the joint torques of a character at every time step. Together with contact forces and external perturbations if there are any, the joint torques are used to update the state of the character. Without including the contact forces in the control vector, our formulation of the trajectory optimization problem enables automatic adjustment of contact timings and positions for balancing in response to environmental changes and external perturbations. For efficiency, we adopt derivative-based trajectory optimization on top of state-of-the-art smoothed contact dynamics. Use of derivatives enables our method to run much faster than the existing sampling-based methods. In order to further accelerate the performance of MPC, we propose efficient numerical differentiation of the system dynamics of a full-body character based on two schemes: data reuse and data interpolation. The former scheme exploits data dependency to reuse physical quantities of the system dynamics at near-by time points. The latter scheme allows the use of derivatives at sparse sample points to interpolate those at other time points in the window. We further accelerate evaluation of the system dynamics by exploiting the sparsity of physical quantities such as Jacobian matrix resulting from the tree-like structure of the articulated body. Through experiments, we show that the proposed method efficiently can synthesize realistic motions such as locomotion, dancing, gymnastic motions, and martial arts at interactive rates using moderate computing resources.

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Section: Related Workmentioning

confidence: 99%

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

Han

Eom

Noh

et al. 2016

Computer Graphics Forum

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“…A quadratic programming problem solves both reference and balance objectives simultaneous [9]. Defining proper objectives is also a difficult problem, even for low-energy motions, such as walking [10].…”

Section: Related Workmentioning

confidence: 99%

Optimization control for biped motion trajectory

Wang

Bao

et al. 2014

2014 International Conference on Audio, Language and Image Processing

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Character animation is important in many applications, such as video games, movie production, and somatosensory interaction. However, it is difficult to control the locomotion of a virtual character in a dynamic environment, because bipeds are under-actuated and unstable. In this paper, we present a novel optimization strategy to improve the realism and stability of virtual character animation. Our trajectory optimization method produces lifelike motions by using the motion capture data. The method describes systematic computation of controllers that can optimize locomotion. We use center of mass (COM) control and foot control to enhance the sense of reality in physical movement, and use tracking control to maintain similarity with the reference motion. Experiment results demonstrate our approach is applicable by simulating walking motion and the optimized motion is both natural and recognizable.

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“…Many methods are based on tracking a reference motion based on motion capture data, as seen in many recent methods: [Sok et al 2007;Yin et al 2007;Muico et al 2009;Lee et al 2010;Kwon and Hodgins 2010;Yin et al 2007;Ye and Liu 2010;Da Silva et al 2008;Liu et al 2010;Coros et al 2011] A second approach is to develop appropriate objective functions without relying on captured data and to then synthesize motions using online or offline optimization. Representative examples of this approach include [Liu and Popović 2002;Macchietto et al 2009;Wang et al 2009;de Lasa et al 2010;Wu and Popović 2010;Borno et al 2013]. A third approach involves users in authoring the shape of the controlled motion, e.g., [Liu and Popović 2002;Yin et al 2007;Coros et al 2010;Nunes et al 2012].…”

Section: Related Workmentioning

confidence: 99%

Diverse motion variations for physics-based character animation

Agrawal

Shen

Panne

2013

Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation

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We present an optimization framework for generating diverse variations of physics-based character motions. This allows for the automatic synthesis of rich variations in style for simulated jumps, flips, and walks. While well-posed motion optimization problems result in a single optimal motion, we explore using underconstrained motion descriptions and then optimizing for diversity. As input, the method takes a parameterized controller for a successful motion instance, a set of constraints that should be preserved, and a pairwise distance metric between motions. An offline optimization then produces a highly diverse set of motion styles for the same task. We demonstrate results for a variety of 2D and 3D physics-based motions and show that this approach can generate compelling new motions.

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Trajectory Optimization for Full-Body Movements with Complex Contacts

Cited by 106 publications

References 57 publications

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

Optimization control for biped motion trajectory

Diverse motion variations for physics-based character animation

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