This research examines whether and how observing prosocial behaviors by robots can influence humans to act more prosocially in spatially complex interactions. Combining behavioral experiments with theoretical simulations using agent-based modeling, we investigated observational learning effects among human participants engaged in a cooperative, token-collecting task. One participant was repeatedly trapped, needing assistance, while the other participant, who was never trapped, could observe the robot's prosocial actions and potentially provide help. Across three experimental conditions--no robot involvement, robot help without explicit intention signaling, and robot help with explicit intention signaling--we assessed participants' awareness of the robot's actions and subsequent helping behaviors. Findings show that explicit signaling notably improved awareness of robot interventions; however, this enhanced awareness had a limited impact on increasing prosocial acts by participants. Qualitative analyses further revealed that individuals already inclined toward prosociality were most attentive to and influenced by robot behavior. Agent-based modeling complemented these empirical results by identifying conditions under which observational learning could promote broader prosocial cascades, emphasizing the critical role of attentional thresholds. These insights underline important considerations and constraints for employing robots to foster collective prosocial behavior through observational learning.