Using model knowledge for learning inverse dynamics

By Duy Nguyen-Tuong, Jan Peters. 2010

210 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015, neutral): “Several prior methods have suggested incorporating knowledge about the dynamics as a prior on the dynamics model [11], [12], [23].”
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model (2016, neutral): “Alternatively, inverse dynamics models learn a mapping from current and next state to an action that achieves the transition between the two [29], [4], [25].”
One-shot learning of manipulation skills with online dynamics adaptation and neural network priors (2015, background): “A single attempt at the task is often insufficient to obtain such an accurate model, and while these challenges can be mitigated by incorporating domain knowledge about the system [3], [4] or demonstrations [5], they make the development of a generalpurpose one-shot learning method difficult.”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, unknown): “Recently, the authors of Nguyen-Tuong & Peters [70] combined the strengths of rbd identification and machine learning methods, using gpr for learning the difference between the parametric mapping and the observed behavior.”
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning (2019, background): “The second direction is to incorporate physics priors into parameterized dynamics models for future state prediction (Nguyen-Tuong and Peters, 2010; Kansky et al., 2017).”
Model learning for robot control: a survey (2011, methodology): “If prior knowledge is given as a parametric model, it can be inserted into nonparametric models in a straightforward way, yielding semiparametric learning approaches [107,148].”
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning (2019, unknown): “…Regression (Calinon et al., 2010; Khansari-Zadeh & Billard, 2011), Gaussian Process Regression (Kocijan et al., 2004; Nguyen-Tuong et al., 2009; Nguyen-Tuong & Peters, 2010), Support Vector Regression (Choi et al., 2007; Ferreira et al., 2007), feedforward-(Jansen, 1994; Lenz et al., 2015;…”
Fast Model Identification via Physics Engines for Data-Efficient Policy Search (2017, methodology): “They have been shown effective in robotic tasks [Peters et al. , 2010].”