This paper investigates how terrain complexity and user interaction modes (teleoperation versus passive observation) influence human cognitive load and trust during interactions with a reinforcement learning−trained legged robot navigating complex terrains. We quantitatively assess cognitive load and trust dynamics using multimodal physiological measurements, including GSR, facial skin temperature, and blink rate combined with self-reported NASA-TLX scores. Participants either actively teleoperated or passively observed the robot traversing flat surfaces, irregular terrains, and stairs within a high-fidelity IsaacGym simulation. Our findings highlight distinct physiological and subjective responses linked to both terrain difficulty and interaction role. Specifically, teleoperators experienced higher cognitive load but stable trust levels, whereas observers showed heightened stress responses and reduced trust during challenging terrain navigation. By combining reinforcement learning robot locomotion with multimodal physiological sensing, this research advances methods for real-time assessment of trust and workload in human-robot interaction, offering insights for designing more adaptive and user-centered robotic systems.