Safety-Aware Shared Autonomy via World-Model Constrained Planning

Abstract

Safety-aware shared autonomy aims to enable an autonomous agent to collaborate with a human operator, completing tasks under safety constraints while maximally preserving human intent. However, existing methods often underperform in long-horizon tasks that require balancing task performance under safety constraints with the degree of intervention—particularly when accurately predicting future unsafe events is critical. This limitation largely stems from their reliance on the current state alone to decide when to intervene and how to modify actions. We propose World Model Assisted Safety Planning (WASP), a model-predictive shared autonomy framework for explicit safety constraint satisfaction. We first formulate a safety-aware world model, and leverage its online predictive reasoning to decide when to intervene by jointly assessing prospective safety violations and degradation in task performance, thereby intervening only when necessary while enforcing safety constraints at decision time. Once intervention is triggered, WASP plans a short-horizon residual correction using world model rollouts, filters out unsafe candidates, and executes the least-deviating correction among the remaining high-return options in a receding-horizon loop. Experiments across diverse vision-based safety-critical domains show that WASP substantially reduces safety violations while preserving task performance and reducing unnecessary interventions over prior shared autonomy baselines.