Deep learning helicopter dynamics models

By Ali Punjani, Pieter Abbeel. 2015

190 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering, Statistical and Nonlinear Physics

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Citations in the corpus

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “…in parametric function approximators, such as NNs, suggest that such methods are worth revisiting [Baranes and Oudeyer, 2013, Fu et al., 2016, Punjani and Abbeel, 2015, Lenz et al., 2015, Agrawal et al., 2016, Gal et al., 2016, Depeweg et al., 2016, Williams et al., 2017, Nagabandi et al.,…”
(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, background): “Conventionally, learning-based approaches to autonomous flight have relied on learning from demonstration [2, 1, 30, 34].”
Model-Based Reinforcement Learning via Meta-Policy Optimization (2018, background): “NNs for model learning offer the potential to scale to higher dimensional problems with impressive sample complexity [22, 23, 24, 25].”
Preparing for the Unknown: Learning a Universal Policy with Online System Identification (2017, background): “More recently, deep neural networks have been applied to learn both forward dynamics [26] and inverse dynamics [6] from the real-world data, which can potentially model more complicated dynamics.”
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model (2016, neutral): “A number of options are available for representation of learned dynamics functions, from linear functions [28] [43], to Gaussian processes [2] [19] [7], to deep neural networks [32] [11].”
One-shot learning of manipulation skills with online dynamics adaptation and neural network priors (2015, background): “Recent work has shown that neural networks can learn very good dynamics models for tasks ranging from helicopter flight [20] to cutting vegetables [12].”
Neural Lander: Stable Drone Landing Control Using Learned Dynamics (2018, methodology): “For example, [5], [6] use DNNs to improve system ID of helicopter aerodynamics, but not for controller design.”
Unmanned Aerial Vehicles for Search and Rescue: A Survey (2023, background): “The papers [169,170] proposed hybrid supervised neural network models for dynamic systems, using ﬂexible modules in non-recurrent and recurrent networks.”
Deep learning in robotics: a review of recent research (2017, background): “Punjani and Abbeel [76] used a function approximating deep learning architecture with rectifiers to model the highly coupled dynamics of a radio-controlled helicopter, which is a challenging analytic derivation and difficult system identification problem.”
From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (2016, background): “They have shown their potential for complex physical scene understanding and feature extraction in various applications like [3], [4], [5], to name a few.”
Planning with Goal-Conditioned Policies (2019, background): “Model-based planning in deep reinforcement learning is a well-studied problem in the context of low-dimensional state spaces [50, 32, 39, 7].”