# Unsupervised state representation learning with robotic priors: a robustness benchmark

arXiv: https://arxiv.org/abs/1709.05185 · HTML: https://mlsanity.com/paper/1709.05185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [A Survey of State Representation Learning for Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17518/) (2025)
- [Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces](https://mlsanity.com/paper/2107.01667/) (2021)
- [Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills](https://mlsanity.com/paper/2405.11380/) (2024)
- [Direct and inverse modeling of soft robots by learning a condensed FEM model](https://mlsanity.com/paper/2307.11408/) (2023)
- [State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping](https://mlsanity.com/paper/2309.11984/) (2023)

## Abstract (arXiv)

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation of the learned representation using nearest neighbors in the state space that allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.