Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
📰 ArXiv cs.AI
Learn to attribute failures in Vision-and-Language Navigation Agents using a capability-oriented approach, improving debugability and reliability in safety-critical applications
Action Steps
- Identify interdependent capabilities in Vision-and-Language Navigation Agents, such as perception, memory, planning, and decision-making
- Apply capability-oriented testing to detect failures and localize deficiencies
- Analyze failure attribution to determine which capability deficiencies cause task failures
- Use the insights gained to improve the design and development of Vision-and-Language Navigation Agents
- Implement targeted testing and debugging strategies to address specific capability deficiencies
Who Needs to Know This
Researchers and engineers working on embodied agents, particularly in Vision-Language Navigation, can benefit from this approach to identify and address capability deficiencies, leading to more reliable and efficient systems
Key Insight
💡 Capability-oriented failure attribution enables targeted debugging and improvement of Vision-and-Language Navigation Agents
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🚀 Improve Vision-and-Language Navigation Agents with capability-oriented failure attribution! 🤖
Key Takeaways
Learn to attribute failures in Vision-and-Language Navigation Agents using a capability-oriented approach, improving debugability and reliability in safety-critical applications
Full Article
Title: Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
Abstract:
arXiv:2604.25161v1 Announce Type: cross Abstract: Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision), making failures difficult to localize and attribute. Existing testing methods are largely system-level and provide limited insight into which capability deficiencies cause task failures. We propose a capability-oriented testing approach that enables failure detection
Abstract:
arXiv:2604.25161v1 Announce Type: cross Abstract: Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision), making failures difficult to localize and attribute. Existing testing methods are largely system-level and provide limited insight into which capability deficiencies cause task failures. We propose a capability-oriented testing approach that enables failure detection
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