Conditional Multi-Stage Failure Recovery for Embodied Agents

• URL: http://arxiv.org/abs/2507.06016v1
• Date: Tue, 08 Jul 2025 14:23:41 GMT
• Title: Conditional Multi-Stage Failure Recovery for Embodied Agents
• Authors: Youmna Farag, Svetlana Stoyanchev, Mohan Li, Simon Keizer, Rama Doddipatla,
• Abstract summary: We introduce a conditional multistage failure recovery framework that employs zero-shot chain prompting.<n>We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance.
• Score: 17.95974193288372
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

Related papers

• Application of LLM Guided Reinforcement Learning in Formation Control with Collision Avoidance [1.1718316049475228]
  Multi-Agent Systems (MAS) excel at accomplishing complex objectives through the collaborative efforts of individual agents.<n>In this paper, we introduce a novel framework that aims to overcome the challenge of designing an effective reward function.<n>By giving large language models (LLMs) on the prioritization of tasks, our framework generates reward functions that can be dynamically adjusted online.
  arXiv  Detail & Related papers  (2025-07-22T09:26:00Z)
• RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism [10.288667305064065]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks.<n>LLMs remain prone to generating hallucinated or outdated responses due to their static internal knowledge.<n>Recent advancements in Retrieval-Augmented Generation (RAG) methods have aimed to enhance models' search and reasoning capabilities.
  arXiv  Detail & Related papers  (2025-06-30T09:02:45Z)
• MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning [33.009759731505746]
  Complex tasks involving tool integration pose significant challenges for Large Language Models.<n> Reflection has emerged as an effective strategy for correcting erroneous trajectories in agentic benchmarks.<n>We propose MIRROR, a framework that consists of both intra-reflection, which critically assesses intended actions before execution, and inter-reflection, which further adjusts the trajectory.
  arXiv  Detail & Related papers  (2025-05-27T03:37:33Z)
• Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning [45.10424242207931]
  Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs)<n>We introduce a novel method ReasonRAG that automatically constructs RAG-ProGuide, a high-quality dataset providing process-level rewards for query generation, evidence extraction, and answer generation.<n>With the process-level policy optimization, the proposed framework empowers LLMs to autonomously invoke search, generate queries, extract relevant evidence, and produce final answers.
  arXiv  Detail & Related papers  (2025-05-20T08:21:00Z)
• Fast Adaptation with Behavioral Foundation Models [82.34700481726951]
  Unsupervised zero-shot reinforcement learning has emerged as a powerful paradigm for pretraining behavioral foundation models.<n>Despite promising results, zero-shot policies are often suboptimal due to errors induced by the unsupervised training process.<n>We propose fast adaptation strategies that search in the low-dimensional task-embedding space of the pre-trained BFM to rapidly improve the performance of its zero-shot policies.
  arXiv  Detail & Related papers  (2025-04-10T16:14:17Z)
• InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems [76.39776789410088]
  This work introduces a framework that combines the strong performance of supervised approaches and the flexibility of zero-shot methods.<n>A novel architectural design seamlessly integrates the degradation operator directly into the denoiser.<n> Experimental results on the FFHQ and ImageNet datasets demonstrate state-of-the-art posterior-sampling performance.
  arXiv  Detail & Related papers  (2025-04-02T12:40:57Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• Why Do Multi-Agent LLM Systems Fail? [91.39266556855513]
  We present MAST (Multi-Agent System Failure taxonomy), the first empirically grounded taxonomy designed to understand MAS failures.<n>We analyze seven popular MAS frameworks across over 200 tasks, involving six expert human annotators.<n>We identify 14 unique failure modes, organized into 3 overarching categories, (i) specification issues, (ii) inter-agent misalignment, and (iii) task verification.
  arXiv  Detail & Related papers  (2025-03-17T19:04:38Z)
• Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement [50.481380478458945]
  Iterative step-level Process Refinement (IPR) framework provides detailed step-by-step guidance to enhance agent training. Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.
  arXiv  Detail & Related papers  (2024-06-17T03:29:13Z)
• Devil's Advocate: Anticipatory Reflection for LLM Agents [53.897557605550325]
  Our approach prompts LLM agents to decompose a given task into manageable subtasks. We implement a three-fold introspective intervention:. Anticipatory reflection on potential failures and alternative remedy before action execution. Post-action alignment with subtask objectives and backtracking with remedy to ensure utmost effort in plan execution.
  arXiv  Detail & Related papers  (2024-05-25T19:20:15Z)
• Building Robust Ensembles via Margin Boosting [98.56381714748096]
  In adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks. We develop an algorithm for learning an ensemble with maximum margin. We show that our algorithm not only outperforms existing ensembling techniques, but also large models trained in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-06-07T14:55:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.