ET tu, CLIP? Addressing Common Object Errors for Unseen Environments

Ye Won Byun, Cathy Jiao, Shahriar Noroozizadeh, Jimin Sun, Rosa Vitiello·June 25, 2024

Summary

The paper introduces ET-CLIP, an enhanced model for embodied instruction following that integrates CLIP encoders to improve generalization in the ALFRED task. Unlike previous methods, ET-CLIP uses CLIP as an auxiliary object detection module, adding a loss during training without affecting inference. Experiments with the Episodic Transformer architecture demonstrate significant improvements in unseen environments, particularly in detecting small objects and understanding rare words. ET-CLIP outperforms the baseline with gains in goal-conditioned success rates, object properties, and rare semantics. The study showcases the benefits of CLIP's vision-language alignment for enhanced object detection and complex instruction understanding, making the model more versatile. The research also touches on related work in natural language processing, visual-linguistic models, and the potential for future advancements in embodied instruction tasks.

Tables

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Introduction
Background
Overview of embodied instruction following tasks
Limitations of existing models in ALFRED task
Objective
To develop a more generalized model for embodied instruction following
Introduce ET-CLIP's novel integration of CLIP for improved performance
Method
Data Collection
ALFRED dataset: Environment and task description
Use of diverse instructions and object scenarios
Data Preprocessing
Integration of CLIP for object detection enhancement
Training approach with CLIP as an auxiliary module
Loss Function
Addition of CLIP-based loss during training without affecting inference
Model Architecture
Episodic Transformer: Description and modifications for ET-CLIP
Experiments
Performance Evaluation
Goal-conditioned success rates
Object properties and rare word understanding
Environment Variability
Testing in unseen environments
Impact on small object detection
Results and Analysis
Significance of ET-CLIP's improvements over baseline
Comparison with state-of-the-art models
Ablation studies on CLIP integration
Related Work
Natural language processing (NLP) models for instructions
Visual-linguistic models like CLIP and their applications
Previous approaches in embodied instruction following
Future Directions
Potential advancements in embodied instruction tasks
Limitations and future research challenges
Applications in real-world scenarios
Conclusion
Summary of ET-CLIP's contributions
Implications for enhancing embodied AI systems
Vision for the role of CLIP in embodied instruction following research.
Basic info
papers
computer vision and pattern recognition
computation and language
robotics
machine learning
artificial intelligence
Advanced features
Insights
How does the study demonstrate the benefits of CLIP's vision-language alignment for embodied instruction following?
What are the improvements achieved by ET-CLIP in the ALFRED task, specifically in unseen environments?
What is the primary focus of the ET-CLIP model introduced in the paper?
How does ET-CLIP differ from previous methods in terms of using CLIP?