Visualizing Movement Control Optimization Landscapes
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[1] Zoran Popovic,et al. Optimal gait and form for animal locomotion , 2009, ACM Trans. Graph..
[2] Zoran Popovic,et al. Discovery of complex behaviors through contact-invariant optimization , 2012, ACM Trans. Graph..
[3] Ker-Chau Li,et al. On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma , 1992 .
[4] Yuval Tassa,et al. DeepMind Control Suite , 2018, ArXiv.
[5] Kenneth O. Stanley,et al. Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning , 2017, ArXiv.
[6] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[7] Michael F. Cohen,et al. Interactive spacetime control for animation , 1992, SIGGRAPH.
[8] Sergey Levine,et al. Guided Policy Search , 2013, ICML.
[9] Julian Togelius,et al. Predictive Physics Simulation in Game Mechanics , 2017, CHI PLAY.
[10] Nikolaus Hansen,et al. Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.
[11] Michiel van de Panne,et al. Learning locomotion skills using DeepRL: does the choice of action space matter? , 2016, Symposium on Computer Animation.
[12] Andrew P. Witkin,et al. Spacetime constraints , 1988, SIGGRAPH.
[13] Nikolaus Hansen,et al. The CMA Evolution Strategy: A Tutorial , 2016, ArXiv.
[14] Wojciech Zaremba,et al. OpenAI Gym , 2016, ArXiv.
[15] Emanuel Todorov,et al. Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.
[16] Emanuel Todorov,et al. General duality between optimal control and estimation , 2008, 2008 47th IEEE Conference on Decision and Control.
[17] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[18] Hans-Georg Beyer,et al. Large Scale Black-Box Optimization by Limited-Memory Matrix Adaptation , 2019, IEEE Transactions on Evolutionary Computation.
[19] C. Karen Liu,et al. Online control of simulated humanoids using particle belief propagation , 2015, ACM Trans. Graph..
[20] Hao Li,et al. Visualizing the Loss Landscape of Neural Nets , 2017, NeurIPS.
[21] Marwan Mattar,et al. Unity: A General Platform for Intelligent Agents , 2018, ArXiv.
[22] Jehee Lee,et al. Simulating biped behaviors from human motion data , 2007, SIGGRAPH 2007.
[23] Emanuel Todorov,et al. Combining the benefits of function approximation and trajectory optimization , 2014, Robotics: Science and Systems.
[24] Jessica K. Hodgins,et al. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces , 2004, ACM Trans. Graph..
[25] C. Karen Liu,et al. Learning symmetric and low-energy locomotion , 2018, ACM Trans. Graph..
[26] Nicholay Topin,et al. Exploring loss function topology with cyclical learning rates , 2017, ArXiv.
[27] M. V. D. Panne,et al. Sampling-based contact-rich motion control , 2010, ACM Trans. Graph..
[28] Kyoungmin Lee,et al. Scalable muscle-actuated human simulation and control , 2019, ACM Trans. Graph..
[29] Michiel van de Panne,et al. Flexible muscle-based locomotion for bipedal creatures , 2013, ACM Trans. Graph..
[30] Nicolas Gaud,et al. A Review and Taxonomy of Interactive Optimization Methods in Operations Research , 2015, ACM Trans. Interact. Intell. Syst..
[31] Kourosh Naderi,et al. Discovering and synthesizing humanoid climbing movements , 2017, ACM Trans. Graph..
[32] Russell N. Carney,et al. Pictorial Illustrations Still Improve Students' Learning from Text , 2002 .
[33] Sergey Levine,et al. DeepMimic , 2018, ACM Trans. Graph..
[34] Aaron Hertzmann,et al. Trajectory Optimization for Full-Body Movements with Complex Contacts , 2013, IEEE Transactions on Visualization and Computer Graphics.
[35] Christopher Vyn Jones,et al. Visualization and Optimization , 1997 .
[36] Yuval Tassa,et al. Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[37] Balaraman Ravindran,et al. EPOpt: Learning Robust Neural Network Policies Using Model Ensembles , 2016, ICLR.
[38] Joe Marks,et al. Spacetime constraints revisited , 1993, SIGGRAPH.
[39] Perttu Hämäläinen,et al. Augmenting sampling based controllers with machine learning , 2017, Symposium on Computer Animation.
[40] Joose Rajamäki,et al. Continuous Control Monte Carlo Tree Search Informed by Multiple Experts , 2019, IEEE Transactions on Visualization and Computer Graphics.
[41] Oriol Vinyals,et al. Qualitatively characterizing neural network optimization problems , 2014, ICLR.
[42] Razvan Pascanu,et al. Sharp Minima Can Generalize For Deep Nets , 2017, ICML.
[43] Nancy S. Pollard,et al. Efficient synthesis of physically valid human motion , 2003, ACM Trans. Graph..
[44] Jaakko Lehtinen,et al. Online motion synthesis using sequential Monte Carlo , 2014, ACM Trans. Graph..