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Top Papers in Dynamics models

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Learning Stable Deep Dynamics Models

Deep networks are commonly used to model dynamical systems, predicting how
the state of a system will evolve over time (either autonomously or in response
to control inputs). Despite the predictive po

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Learning Stabilizable Deep Dynamics Models

When neural networks are used to model dynamics, properties such as stability
of the dynamics are generally not guaranteed. In contrast, there is a recent
method for learning the dynamics of autonomou

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Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems

Learning how complex dynamical systems evolve over time is a key challenge in
system identification. For safety critical systems, it is often crucial that
the learned model is guaranteed to converge t

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Learning Dynamics Models for Model Predictive Agents

Model-Based Reinforcement Learning involves learning a \textit{dynamics
model} from data, and then using this model to optimise behaviour, most often
with an online \textit{planner}. Much of the recen

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Learning Dynamics Models with Stable Invariant Sets

Invariance and stability are essential notions in dynamical systems study,
and thus it is of great interest to learn a dynamics model with a stable
invariant set. However, existing methods can only ha

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Planning from Pixels using Inverse Dynamics Models

Learning task-agnostic dynamics models in high-dimensional observation spaces
can be challenging for model-based RL agents. We propose a novel way to learn
latent world models by learning to predict s

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Understanding Compounding Error in Model-Based Reinforcement Learning

Investigating Compounding Prediction Errors in Learned Dynamics Models

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Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction

A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces. In order to scale l

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Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models

Deep learning has been widely used within learning algorithms for robotics.
One disadvantage of deep networks is that these networks are black-box
representations. Therefore, the learned approximation

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Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Recent work has shown deep learning can accelerate the prediction of physical
dynamics relative to numerical solvers. However, limited physical accuracy and
an inability to generalize under the distri

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Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Standard dynamics models for continuous control make use of feedforward
computation to predict the conditional distribution of next state and reward
given current state and action using a multivariate

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Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

We propose a method to predict the sim-to-real transfer performance of RL
policies. Our transfer metric simplifies the selection of training setups (such
as algorithm, hyperparameters, randomizations)

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Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

Noisy observations coupled with nonlinear dynamics pose one of the biggest
challenges in robot motion planning. By decomposing nonlinear dynamics into a
discrete set of local dynamics models, hybrid d

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A Novel Model of Opinion Dynamics

Opinion Dynamics Models with Memory in Coopetitive Social Networks: Analysis, Application and Simulation

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The choice of the scale affects the dynamics of opinion dynamics models

A new degree of freedom for opinion dynamics models: the arbitrariness of scales

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Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control

Modelling robot dynamics accurately is essential for control, motion
optimisation and safe human-robot collaboration. Given the complexity of modern
robotic systems, dynamics modelling remains non-tri

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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Model-based reinforcement learning (RL) algorithms can attain excellent
sample efficiency, but often lag behind the best model-free algorithms in terms
of asymptotic performance. This is especially tr

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Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

Estimating accurate forward and inverse dynamics models is a crucial
component of model-based control for sophisticated robots such as robots driven
by hydraulics, artificial muscles, or robots dealin

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End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control

It is well-known that inverse dynamics models can improve tracking
performance in robot control. These models need to precisely capture the robot
dynamics, which consist of well-understood components,

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Offline Model-based Reinforcement Learning with Adaptive Priors

Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL

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