Real-world physical human-robot interaction (pHRI) development is costly and slow, while simulation requires substantial task-specific scene and motion authoring. We present GenPHRI, a generative simulation framework for producing deployment-ready pHRI scenarios entirely from natural-language task descriptions using vision-language model (VLM) agents. Generated scenarios contain a room with furniture, a task-appropriate human pose and placement, and a robot executing the requested interaction. Generator and critic agents iterate on each component, and an orchestrator agent controls cross-stage decisions. With GenPHRI, we generate 50 different assistive scenarios in simulation with no manual intervention and deploy two tasks in a 12-participant user study. Vision policies trained on these scenarios achieve zero-shot task completion rates of ~80% in real-world trials against ~90% in simulation, while producing behaviors that participants rate as consistent with textual task prompts. Further, we demonstrate direct execution of generated motion as a complementary, training-free deployment pathway that enables rapid real-world inspection of generated trajectories at the cost of contact coverage. We release GenPHRI as a platform for generating, training, and deploying pHRI tasks from textual prompts. Additional information may be found on our project website: https://rchi-lab.github.io/gen_phri/

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