Augmenting Imitation Experience via Equivariant Representations (2110.07668v1)

Abstract: The robustness of visual navigation policies trained through imitation often hinges on the augmentation of the training image-action pairs. Traditionally, this has been done by collecting data from multiple cameras, by using standard data augmentations from computer vision, such as adding random noise to each image, or by synthesizing training images. In this paper we show that there is another practical alternative for data augmentation for visual navigation based on extrapolating viewpoint embeddings and actions nearby the ones observed in the training data. Our method makes use of the geometry of the visual navigation problem in 2D and 3D and relies on policies that are functions of equivariant embeddings, as opposed to images. Given an image-action pair from a training navigation dataset, our neural network model predicts the latent representations of images at nearby viewpoints, using the equivariance property, and augments the dataset. We then train a policy on the augmented dataset. Our simulation results indicate that policies trained in this way exhibit reduced cross-track error, and require fewer interventions compared to policies trained using standard augmentation methods. We also show similar results in autonomous visual navigation by a real ground robot along a path of over 500m.

• The paper introduces a novel two-phase training method that uses equivariant representations to augment data, enhancing robustness in visual navigation policies learned via imitation.
• Experiments on ground vehicles and UAVs showed significant improvements, including reduced error and interventions, demonstrating improved generalization to novel environments.
• This technique offers practical advancements for autonomous systems by addressing data limitations and contributes theoretically to the understanding of equivariant representations in reinforcement learning.

Equivariant Data Augmentation for Robust Imitation Learning in Visual Navigation

The paper "Augmenting Imitation Experience via Equivariant Representations" by Dhruv Sharma et al. provides an in-depth examination of a novel data augmentation technique specifically designed for visual navigation tasks. The authors propose a method that leverages equivariant representations to enhance the robustness and generalization of visual navigation policies derived through imitation learning.

Summary of Methodology

The core contribution of this work lies in a two-phase training process. Initially, the model learns equivariant representations by mapping images from a central camera viewpoint to related viewpoints, such as those captured from slightly shifted positions. This phase involves training an encoder to generate latent representations of camera images, which are then used to predict embeddings from nearby viewpoints via learned equivariant maps (i.e., functions that account for the geometric relationships between viewpoints).

The second phase focuses on policy training, where these enriched datasets (comprising both original and newly generated embeddings) are used to train visual navigation policies. The policies aim to map these embeddings to corresponding navigation actions, enhancing robustness by exposure to a wider distribution of input states.

Notable Results and Implications

Experimental validation included autonomous ground vehicles and Unmanned Aerial Vehicles (UAVs), with significant improvements observed across both domains. Particularly notable was the reduction in cross-track error and the decreased number of required human interventions compared to standard augmentation methods. Furthermore, the approach was validated in both simulation environments and through real-world testing on a terrestrial robot covering over 0.5 km.

These findings emphasize the practical significance of utilizing equivariant representations for visual navigation. Notably, the method exhibits improved generalization to novel environments, suggesting that the learned features capture robust structural relationships within the data.

Practical and Theoretical Implications

From a practical standpoint, this approach offers significant advancements in deploying autonomous systems reliant on visual input, such as self-driving cars and UAVs. By expanding the effective training set through learned transformations, the method addresses traditional limitations regarding data scarcity and distributional shifts — key challenges in ensuring reliability and safety in real-world deployments.

Theoretically, the work advances the understanding of equivariant representations in neural networks and their utility in reinforcement learning tasks. The integration of such structural priors into the learning process can be extended to other domains where reliable generalization from limited viewpoints is required.

Potential for Future Developments

Future research paths might explore further scalability of this technique by incorporating other forms of domain knowledge or by leveraging self-supervised learning paradigms to circumvent the dependency on labeled datasets. Additionally, exploring the structured design of equivariant networks, directly encoding viewpoint transformations into their architecture, presents a promising avenue to enhance computational efficiency.

Overall, this paper contributes meaningfully to the progression of data-efficient, robust visual navigation frameworks and provides a foundation for subsequent advancements in imitation learning and autonomous systems.