Indoor　fires　pose　a　significant　challenge　to　the　safe　evacuation　of　pedestrians.　In　response　to　fire　hazards,　pedestrians　instinctively　seek　alternative　evacuation　routes　to　avoid　the　hazard　zone.　However,　the　specific　location　and　intensity　of　the　fire　hazard　zone　can　influence　pedestrians＇　decisions,　leading　to　varying　congestion　levels　in　different　areas.　To　address　this　challenge　and　enhance　overall　evacuation　efficiency,　this　paper　introduces　an　improved　social　force　model　to　depict　pedestrian　movement　in　fire　scenarios　and　proposes　a　methodology　that　leverages　dynamic　robot　for　pedestrian　evacuation,　employing　Deep　Reinforcement　Learning　(DRL)　and　Human-Robot　Interaction　(HRI).　The　results　show　that　in　the　no-robot　scenario,　pedestrians　will　detour　according　to　the　varying　locations　of　fire　hazard　zones　and　emergency　levels,　resulting　in　congestion　at　different　positions.　In　the　static　robot　scenario,　robots　placed　in　different　locations　exhibit　varied　effects　on　evacuation　depending　on　the　fire　hazard　zones＇　locations　and　intensities.　In　the　DRL-control　robot　scenario,　the　robot　controlled　by　DRL　and　HRL　can　always　navigate　to　the　appropriate　position　to　promote　evacuation,　regardless　of　the　fire＇s　location　and　emergency　levels　or　the　robot＇s　initial　placement.　Furthermore,　our　findings　reveal　that　strategically　positioned　robots　can　enhance　evacuation　efficiency　by　alleviating　crowding　and　increasing　the　distance　between　pedestrians　and　fire　hazard　zones　in　most　cases,　thereby　improving　pedestrian　safety.　This　study　offers　practical　guidance　for　managing　pedestrian　evacuation　during　fire　incidents　and　establishes　a　theoretical　foundation　for　refining　evacuation　strategies　and　safety　measures　at　fire　scenes.　©　2024　The　Physical　Society　of　the　Republic　of　China　(Taiwan)