Automatic Gait Optimization with Gaussian Process Regression

By Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans. 2007

341 citations recorded by the source corpus.

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “In [28], [26], the authors use Bayesian optimization to set the parameters of several animation systems by showing the user examples of different parametrized animations and asking for feedback.”
Reinforcement learning in robotics: A survey (2013, methodology): “For example, a visionbased mobile robot docking task can be learned faster with such a basic behavior than using Q-learning alone, as demonstrated by Martínez-Marín and Duckett (2005) Providing hand-coded, stable initial gaits can significantly help in learning robot locomotion, as shown on a six-le”
Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, background): “, 2012), optimal policy search (Lizotte et al., 2007; Martinez-Cantin et al., 2007) and scientific experiments (Gonzalez et al.”
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (2012, methodology): “, 1998), Maximum Probability of Improvement (Mockus, 1989), and upperconfidence based methods (Cox & John, 1997), have been developed to balance exploration with exploitation and successfully applied in learning problems (Lizotte et al., 2007).”
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions (2014, methodology): “Bayesian optimization has successfully been used in robotics to adjust the parameters of a robot’s controller to maximize gait speed and smoothness [16] as well as parameter tuning for computer graphics [6].”
Contextual Gaussian Process Bandit Optimization (2011, background): “Suppose we have a complex system and would like to achieve some desired behavior, for example robot walking [10].”
Deep Kernels for Optimizing Locomotion Controllers (2017, background): “However, [5, 20, 3] used snake robots, which are statically stable, making discontinuities in the cost function landscape less likely and in turn making the optimization easier.”
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters (2014, background): “Bayesian optimisation has become an important area of research and development in the field of machine learning, as evidenced by recent media coverage [8] and a blossoming range of applications to interactive user-interfaces [4], robotics [16, 19], environmental monitoring [18], information extracti”
Optimization as Estimation with Gaussian Processes in Bandit Settings (2015, methodology): “The practical benefits of Bayesian optimization have been shown in a number of applications [3, 6, 19, 29, 33, 34].”
Bayesian optimization for learning gaits under uncertainty (2015, methodology): “In [9], two gait optimization criteria are considered for a Sony AIBO The location of the next parameter to be evaluated is represented by the vertical green dashed line.”
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions (2015, background): “Bayesian optimization [4] has been successfully applied in a range of difﬁcult, expensive global optimization tasks including optimizing a robot controller to maximize gait speed [5] and discovering a chemical derivative of a particular molecule which best treats a particular disease [6].”
Black-box data-efficient policy search for robotics (2017, background): “It can drastically decrease the evaluation time when optimizing gaits [10] or when finding compensatory behaviors for damaged robots [2].”
Learning Motion Skills from Expert Demonstrations and Own Experience using Gaussian Process Regression (2010, methodology): “[6] proposed to select actions probabilistically, based on Gaussian Process Regression and the most probable improvement criterion.”
Safe controller optimization for quadrotors with Gaussian processes (2015, background): “Examples include gait optimization of legged robots [10], [11] and controller optimization for a snake-like robot [12].”
Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013, methodology): “…the machine learning community has increasingly used Bayesian optimization (Rasmussen, 2003; Brochu et al., 2007; Martinez-Cantin et al., 2007; Lizotte et al., 2007; Frazier et al., 2009; Azimi et al., 2010; Hamze et al., 2011; Azimi et al., 2011; Bergstra et al., 2011; Gramacy & Polson,…”