We address the problem of intention estimation in human-robot teleoperation, which involves identifying the task being completed and predicting the next actions. Our approach sequentially quantifies the similarity between the observed action sequence and nominal action sequences representing possible tasks using the edit distance metric. Task estimation and action prediction are then performed using a nearestneighbor rule. A key advantage of our approach is its robustness to deviations in operator actions and action recognition errors, commonly encountered in real-world teleoperation settings. Through extensive experiments on both real and simulated data, we demonstrate that our method largely outperforms alternative approaches, including probabilistic graphical models and transformer-based methods, particularly in scenarios with significant action deviations or action recognition errors. Additionally, we construct task distance matrices to analyze task similarities and potential confusion points, providing insights into when and where estimation errors are likely to occur. This analysis can guide the design of more distinctive task sequences and further improve the reliability of teleoperated robotic systems.