Vision-Language Navigation with Transformer Architectures

Transformer-based agent for navigating complex environments using natural language instructions

Project Overview:

This project addresses the challenge of vision-language navigation (VLN), where an agent must navigate to a target location following natural language instructions. The approach uses a transformer-based architecture to integrate visual, linguistic, and spatial information, enabling robust navigation in complex, previously unseen environments.

Key Contributions:

  • Developed a multi-modal transformer architecture that jointly processes visual and linguistic inputs
  • Implemented a hierarchical planning module that breaks down navigation into macro and micro actions
  • Created a novel attention mechanism that grounds language instructions to visual features
  • Designed a pre-training strategy using a combination of web-scale image-text pairs and navigation data
  • Evaluated the approach on standard VLN benchmarks including R2R, REVERIE, and SOON datasets

Technical Implementation:

The core architecture consists of:

  1. Visual Encoder: A vision transformer processes panoramic images at each viewpoint, extracting features at multiple scales.

  2. Language Understanding Module: A language transformer encodes instructions and maintains an attention-based progress monitor.

  3. Cross-Modal Reasoning: A fusion transformer integrates visual and linguistic features, generating a representation that guides navigation decisions.

  4. Action Predictor: A hierarchical decision module that first selects high-level directions and then refines to specific viewpoints.

The model was implemented in PyTorch and trained using a combination of imitation learning and reinforcement learning, with a curriculum that gradually increased the complexity of navigation scenarios.

Results:

  • 15% improvement in success rate on the R2R benchmark compared to previous methods
  • 12% higher success rate on the REVERIE dataset for object-grounded navigation
  • Effective zero-shot transfer to unseen environments
  • Robust performance with ambiguous and complex language instructions

The project demonstrates the effectiveness of transformer architectures for embodied AI tasks, particularly those requiring multi-modal reasoning and long-horizon planning.