How Much Progress Did I Make? An Unexplored Human Feedback Signal for Teaching Robots
This work addresses the limitations of conventional human feedback signals—namely, low discriminability, difficulty in quantification, and poor inter-user consistency—for teaching robots to non-expert users. We propose *progress* as a novel, task-agnostic feedback signal, formally defined and empirically validated as a normalized task completion percentage. This signal exhibits strong discriminability between success and failure, enables precise quantification of partial progress, identifies harmless but inefficient behaviors, achieves high cross-user consistency, and imposes zero additional annotation burden. We validate its efficacy and robustness through three empirical studies: an online crowdsourced experiment (N=76), an in-situ user study in public spaces (N=40), and a real-world ice cream topping task. Concurrently, we introduce and publicly release the first benchmark dataset of 40 novice demonstrations, annotated with exploratory actions and operational errors—designed explicitly to support progress-based learning.