Conditional Multi-Stage Failure Recovery for Embodied Agents

2025-07-08

AI Summary

Embodied agents frequently fail in complex tasks due to environmental dynamics, necessitating efficient, training-free recovery mechanisms. This paper proposes the Conditional Multi-Stage Recovery Framework (CMRF), introducing a novel four-stage error-handling pipeline—comprising three real-time execution-phase responses and one post-execution reflective stage. CMRF integrates zero-shot chain-of-thought prompting with environment feedback-driven conditional reasoning to enable context-aware, dynamic error correction and task recovery grounded in large language models (LLMs). Crucially, it requires no fine-tuning or additional training. Evaluated on the TEACH-TfD benchmark, CMRF achieves a 11.5% absolute improvement over the no-recovery baseline and outperforms the previous state-of-the-art by 19%, establishing a new SOTA. The framework significantly enhances robustness while preserving computational efficiency and deployment simplicity.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Embodied AI

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditional multistage failure recovery framework that employs zero-shot chain prompting. The framework is structured into four error-handling stages, with three operating during task execution and one functioning as a post-execution reflection phase. Our approach utilises the reasoning capabilities of LLMs to analyse execution challenges within their environmental context and devise strategic solutions. We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance, outperforming a baseline without error recovery by 11.5% and surpassing the strongest existing model by 19%.

Problem

• Develops multi-stage recovery for embodied agent failures
• Uses LLMs to analyze and solve execution challenges
• Improves performance by 19% over existing models

Innovation

• Conditional multistage failure recovery framework
• Zero-shot chain prompting technique
• LLM reasoning for strategic solutions