LeadVLA: Learning Follower-Aware Route Execution for Robot Leading

Robot leading requires executing a route while continuously adapting to a designated follower. Existing robot-leading systems often mediate follower understanding and physical route execution through explicit states, planners, or modular interfaces; directly learning this coupling through end-to-end trajectory prediction can instead entangle route execution with follower response. We introduce LeadVLA, a structured multimodal policy that preserves the route as an explicit Path reference while learning a Pace for route progress and a Residual for local follower- and scene-conditioned adaptation. To make this problem trainable and measurable, we develop LeadInfra, a configurable Unreal Engine-based closed-loop data-generation infrastructure from which we instantiate an approximately 4M-sample training corpus, and LeadBench, a standardized benchmark with 108 fully held-out closed-loop episodes. LeadVLA achieves the highest task success (39.6%) among evaluated robot policies on LeadBench while maintaining strong route fidelity and follower responsiveness, and transfers to unseen real-world environments without real-world policy fine-tuning. **Project page**: https://anonymous44paper.github.io/LeadVLA/#overview.