# Keep It Simple: Data-Efficient Learning for Controlling Complex Systems With Simple Models

@article{Power2021KeepIS, title={Keep It Simple: Data-Efficient Learning for Controlling Complex Systems With Simple Models}, author={Thomas Power and Dmitry Berenson}, journal={IEEE Robotics and Automation Letters}, year={2021}, volume={6}, pages={1184-1191} }

When manipulating a novel object with complex dynamics, a state representation is not always available, for example, deformable objects. Learning both a representation and dynamics from observations requires large amounts of data. We propose Learned Visual Similarity Predictive Control (LVSPC), a novel method for data-efficient learning to control systems with complex dynamics and high-dimensional state spaces from images. LVSPC leverages a given simple model approximation from which image…

## 7 Citations

### Goal-Conditioned Model Simplification for Deformable Object Manipulation

- Computer Science
- 2022

This work explores the idea of goal-conditioned model simplification which has a great potential to improve motion planning, perception, and policy learning and proposes two workflows for objects that can be approximated by lines and surfaces.

### Planning with Learned Model Preconditions for Water Manipulation

- Computer Science
- 2022

This work addresses the problem of modeling deformable object dynamics by learning where a set of given high-level dynamics models are accurate: a model precondition, which is then used to model trajectories using states and closed-loop actions where the dynamics model are accurate.

### Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection

- Computer ScienceRobotics: Science and Systems XVIII
- 2022

A Model Predictive Control method for collision-free navigation that uses amortized variational inference to approximate the distribution of optimal control sequences by training a normalizing flow conditioned on the start, goal and environment and an approach that performs projection on the representation of the environment as part of the MPC process is presented.

### Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility

- Computer Science2022 International Conference on Robotics and Automation (ICRA)
- 2022

A model that can perform contact-rich flexible object manipulation by real-time prediction of vision with tactility was developed, which introduced a point-based attention mechanism for extracting image features, softmax transformation for predicting motions, and convolutional neural network for extracting tactile features.

### Challenges and Outlook in Robotic Manipulation of Deformable Objects

- EngineeringIEEE Robotics & Automation Magazine
- 2022

This article reviews recent advances in deformable object manipulation and highlights the main challenges when considering deformation in each sub-field, and proposes future directions of research.

### Variational Inference MPC for Robot Motion with Normalizing Flows

- Computer Science
- 2021

This paper proposes using amortized variational inference to approximate the posterior with a normalizing conditioned on the start, goal and environment and demonstrates that this approach generalizes to a difﬁcult novel environment and outperform a baseline sampling-based MPC method on a navigation problem.

### Learning Model Preconditions for Planning with Multiple Models

- Computer ScienceCoRL
- 2021

This work learns model deviation estimators (MDEs) to predict the error between real-world states and the states outputted from skill effect models and uses the prediction from MDEs to switch between various models in order to speed up planning when possible.

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