Robots are often designed to help, but help is not always helpful. In everyday situations, it is a socially delicate act: the right offer of help at the wrong moment can be intrusive, unnecessary, or even undermining. In this paper, we challenge the prevailing assumption that robots should always offer help, prompting an essential discussion of how robots can discern when to offer help. We introduce a theoretical framework that enables robots to assess the appropriateness of offering help by considering factors such as the relative skill levels of the robot and human user, as well as the social value and cost of assistance. To validate this framework, we conducted a large-scale online study in which participants rated the appropriateness of robot assistance across diverse task scenarios. Their responses supported our core predictions and highlighted additional contextual factors. Building on these results, we discuss potential extensions of the simplified model for real-world settings, including uncertainty management, perception of ability, autonomy preferences, and social presence. We present these directions as opportunities for future research.
Ramnauth et al. (2026) studied this question.