Unsupervised state representation learning with robotic priors: a robustness benchmark (1709.05185v1)

Abstract: 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.

• The paper introduces a novel unsupervised learning approach that incorporates robotic priors to generate coherent, low-dimensional state representations.
• The paper employs a siamese neural network architecture and distinct priors such as temporal coherence and causality to enforce robust learning from high-dimensional visual data.
• The paper demonstrates marked performance improvements over baselines using KNN-MSE and NIEQA metrics, emphasizing its practical benefits for complex robotic environments.

Unsupervised State Representation Learning with Robotic Priors: A Robustness Benchmark

The paper "Unsupervised State Representation Learning with Robotic Priors: A Robustness Benchmark" introduces a methodological approach to state representation learning using unsupervised techniques augmented with robotic priors. This approach leverages general knowledge about the world encapsulated in the form of loss functions—termed as robotic priors—to derive coherent, low-dimensional state representations from captured robotic images. This work is grounded in the context of the increasing complexity of environments where robotic systems operate. Given the continuous state space and the dynamic nature of such environments, the ability to learn useful representations is pivotal for efficient robotic task execution and transfer learning.

Methodology and Innovations

At the core of this research is the use of robotic priors for unsupervised learning. These priors serve as a form of regularization within the learning process by maintaining consistency with physical and causal effects observed in robot environments. The paper builds upon the foundation of previous work by Jonschkowski et al., extending their use of robotic priors to richer data representations from high-dimensional image inputs. The researchers develop a siamese neural network architecture to encode state representation learning and enforce these priors, allowing for the acquisition of 3D representations from raw RGB images without the requirement of direct state supervision.

The methodology is deeply rooted in several distinct priors:

1. Temporal Coherence: Enforcing states that are temporally adjacent to be closer in space.
2. Proportionality: Requiring that similar actions lead to proportionately similar state changes.
3. Repeatability: Ensuring consistency of state changes resulting from repeated actions.
4. Causality: Disallowing states that result in different subsequent rewards to be proximate.

Additionally, a novel reference point prior is introduced to mitigate issues like sequence clustering, which occurs when state representations learned from different data sequences do not generalize well across varying environmental conditions or object disguises.

Results and Evaluation

The paper presents a rigorous experimental analysis across multiple datasets, including complex 2D and 3D environments with various distractors and domain randomizations. The results are quantitatively assessed using KNN-MSE (K-Nearest Neighbors Mean Squared Error) and NIEQA (Nonlinear Intrinsic and Extrinsic Quality Assessment) metrics, providing robust evaluations of the efficacy of the learned state spaces.

Across these evaluations, the robotic priors approach significantly outperformed baseline models, such as denoising autoencoders, in generating more task-relevant state representations. In particular, the introduction of the reference point prior showed marked improvements in scenarios involving static distractors or where clustering of representations based on sequence alignment was previously problematic.

Implications and Future Directions

This research contributes a vital step towards unsupervised learning approaches in robotics that do not rely on precisely labeled data or predefined task-specific features. The implications of this work span practical applications such as transfer learning from simulated to real-world environments and theoretical advancements in understanding the bounds and efficacy of robotic priors.

Future work can investigate the integration of these priors with other state space learning methods or apply the approach in more complex settings involving multiple robots or dynamic, interactive elements. Additionally, leveraging unsupervised state representation learning in conjunction with reinforcement learning algorithms presents a promising area for developing autonomous systems capable of adaptive learning across diverse and evolving conditions.

In conclusion, the research outlined offers a comprehensive framework and benchmark for unsupervised state representation in robotics, fundamentally advocating for the use of physical priors to deepen our understanding and competence in robotic learning paradigms.