Robust Intervention Learning from Emergency Stop Interventions

2026-02-03

AI Summary

This work proposes the Residual Intervention Fine-Tuning (RIFT) framework to address emergency stop intervention signals that are noisy and informationally incomplete. RIFT formulates intervention learning as a residual optimization problem relative to a prior policy, thereby enabling policy refinement even under ambiguous task definitions. It is the first approach to formally characterize robust intervention learning by jointly leveraging incomplete intervention signals and the structural knowledge embedded in the prior policy. Theoretical analysis establishes sufficient conditions for performance improvement and delineates failure boundaries. Extensive experiments demonstrate that RIFT consistently enhances policy performance across diverse intervention types and varying qualities of prior policies, confirming its effectiveness and generalization capability.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Human interventions are a common source of data in autonomous systems during testing. These interventions provide an important signal about where the current policy needs improvement, but are often noisy and incomplete. We define Robust Intervention Learning (RIL) as the problem of learning from intervention data while remaining robust to the quality and informativeness of the intervention signal. In the best case, interventions are precise and avoiding them is sufficient to solve the task, but in many realistic settings avoiding interventions is necessary but not sufficient for achieving good performance. We study robust intervention learning in the context of emergency stop interventions and propose Residual Intervention Fine-Tuning (RIFT), a residual fine-tuning algorithm that treats intervention feedback as an incomplete learning signal and explicitly combines it with a prior policy. By framing intervention learning as a fine-tuning problem, our approach leverages structure encoded in the prior policy to resolve ambiguity when intervention signals under-specify the task. We provide theoretical analysis characterizing conditions under which this formulation yields principled policy improvement, and identify regimes where intervention learning is expected to fail. Our experiments reveal that residual fine-tuning enables robust and consistent policy improvement across a range of intervention strategies and prior policy qualities, and highlight robust intervention learning as a promising direction for future work.

Problem

• Robust Intervention Learning
• Emergency Stop Interventions
• Autonomous Systems
• Intervention Data
• Policy Learning

Innovation

• Robust Intervention Learning
• Residual Intervention Fine-Tuning
• Emergency Stop Interventions
• Policy Fine-tuning
• Human-in-the-loop Learning