Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks

2025-03-28

AI Summary

Existing procedural task error detection methods rely on static, single-action prototypes, rendering them inadequate for scenarios where multiple valid successor actions exist given the same antecedent sequence—leading to poor cross-environment generalization and prototype misalignment. This paper proposes the Adaptive Multi-Normal Action Representation (AMNAR) framework, the first to dynamically predict and reconstruct contextualized representations of *all* valid successor actions conditioned on the current state sequence. AMNAR integrates temporal modeling, multi-branch representation learning, and online reconstruction-based comparison to enable context-aware, real-time error discrimination. Evaluated on multiple procedural task benchmarks, AMNAR achieves state-of-the-art performance, significantly improving both cross-environment error detection accuracy and robustness. The implementation is publicly available.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Error detection in procedural activities is essential for consistent and correct outcomes in AR-assisted and robotic systems. Existing methods often focus on temporal ordering errors or rely on static prototypes to represent normal actions. However, these approaches typically overlook the common scenario where multiple, distinct actions are valid following a given sequence of executed actions. This leads to two issues: (1) the model cannot effectively detect errors using static prototypes when the inference environment or action execution distribution differs from training; and (2) the model may also use the wrong prototypes to detect errors if the ongoing action label is not the same as the predicted one. To address this problem, we propose an Adaptive Multiple Normal Action Representation (AMNAR) framework. AMNAR predicts all valid next actions and reconstructs their corresponding normal action representations, which are compared against the ongoing action to detect errors. Extensive experiments demonstrate that AMNAR achieves state-of-the-art performance, highlighting the effectiveness of AMNAR and the importance of modeling multiple valid next actions in error detection. The code is available at https://github.com/iSEE-Laboratory/AMNAR.

Problem

• Detecting errors in procedural tasks with multiple valid actions
• Overcoming limitations of static prototypes in dynamic environments
• Improving error detection by predicting all valid next actions

Innovation

• Adaptive Multiple Normal Action Representation
• Predicts all valid next actions
• Compares representations for error detection