Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

Visual navigation research has evolved from classical path planning to deep learning-based end-to-end models. Early methods relied on handcrafted features and static maps, limiting scalability. The advent of deep reinforcement learning (e.g., DQN, A3C) enabled agents to learn from raw sensory data, but struggled with generalization. The introduction of the Room-to-Room (R2R) dataset, using real-world Matterport3D scenes, marked a significant milestone by incorporating natural language instructions and realistic visuals. Despite progress, models still overfit to training environments, performing poorly on unseen scenes. Recent efforts include data augmentation, domain adaptation, and semi-supervised learning, yet the challenge of environmental variability remains. This paper builds on these foundations, proposing environment Dropout and back-translation to address the core generalization bottleneck.

The key challenge in embodied visual navigation is enabling models to generalize from seen to unseen environments. Existing models excel in familiar scenes but falter when encountering new layouts, objects, or visual conditions. This gap stems from limited environmental diversity in training data and insufficient simulation of real-world variability. Consequently, models overfit to specific scene features, losing robustness in novel settings. Addressing this requires methods that can simulate unseen environments during training, allowing models to learn invariant features and adapt to diverse scenarios. The difficulty lies in creating realistic, scalable environment augmentation techniques that do not compromise training efficiency or data quality.

The paper introduces environment Dropout, a novel data augmentation technique that masks visual features to simulate environmental changes, akin to removing or hiding objects in the scene. This approach enhances model robustness by exposing it to virtual environments during training. Coupled with a back-translation module—using a bidirectional LSTM speaker to generate diverse instructions for generated routes—the method enriches the training set with synthetic, varied data. Unlike traditional augmentation, environment Dropout operates at the feature level, maintaining spatial and connectivity structures, thus producing realistic environment variations. The integration of IL and RL further refines navigation policies, resulting in a comprehensive framework that significantly improves generalization performance.

• �� First, train the agent via a mixture of imitation learning (IL) and reinforcement learning (RL), where IL mimics expert demonstrations, and RL optimizes reward signals for successful navigation.
• �� Implement environment Dropout by applying a Bernoulli mask to visual features, randomly occluding parts of the scene to mimic environmental variability.
• �� Develop a back-translation model with a stacked bidirectional LSTM speaker, which generates instructions from routes, enabling synthetic data creation.
• �� Use the speaker to produce instructions for routes in both original and environment-dropout simulated environments, creating new triplets (environment, route, instruction).
• �� Fine-tune the agent with this augmented data, leveraging IL+RL to improve robustness and generalization.
• �� During training, share Dropout masks across environments in a batch to stabilize learning and prevent overfitting.

The dataset used is Matterport3D's Room-to-Room, split into training, validation, and unseen test sets. Evaluation metrics include success rate (SR), SPL, navigation length (NL), and navigation error (NE). Baselines include prior models like Fried et al. (2018) and Wang et al. (2019). Hyperparameters such as Dropout rate p=0.3, training for 50 epochs with early stopping, were used. Ablation studies compared models with and without environment Dropout and back-translation. The model's success rate on unseen test environments reached 68.9%, outperforming previous methods by a margin. Multiple experimental setups, including single run, beam search, and pre-exploration, validated robustness.

The proposed model achieved a success rate of 68.9% on unseen environments, surpassing the previous best (Wang et al., 2019) by nearly 6%. In single-run scenarios, success rate was 51.5%, a 3.5% increase over prior models. Ablation results confirmed that environment Dropout and back-translation contributed over 4% improvements individually, and their combination yielded the highest gains. The model also demonstrated shorter navigation lengths and lower errors, indicating more efficient and accurate navigation. These results validate the effectiveness of environment simulation and synthetic data augmentation in enhancing generalization.

This approach can be directly applied to autonomous robots operating in homes, offices, and public spaces, where environmental variability is high. It enables robots to understand natural language commands and navigate reliably without extensive environment-specific training. Additionally, virtual assistants and VR-based training systems can benefit from robust navigation capabilities. Long-term, this methodology supports the development of adaptable, scalable embodied AI systems capable of functioning in diverse, unpredictable real-world scenarios, reducing the need for environment-specific data collection.

While environment Dropout effectively simulates variability, it cannot fully replicate complex, dynamic real-world environments with moving objects or changing lighting. The quality of synthetic instructions depends on the speaker model, which may introduce biases or errors. Computational complexity increases due to multi-stage training, limiting real-time deployment. Moreover, the approach's effectiveness diminishes in highly cluttered or outdoor environments, requiring further enhancement of environment simulation techniques.