HRC-SimBench: Benchmarking Interaction-Centric Human-Robot Collaboration

Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled. However, real-world collaboration fundamentally requires coordination under shared agency, including intent understanding, temporal synchronization, protocol adherence, and safe interaction in dynamic environments. To address this gap, we introduce HRC-SimBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable interaction scenarios. HRC-SimBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions. Building on this abstraction, HRC-SimBench defines three representative interaction roles: Instructor, Collaborator, and Intruder, covering intent communication, joint coordination, and robustness under human intervention. The benchmark contains 13 role-conditioned tasks with over 650 evaluation episodes generated from diverse interaction trajectories and scene variations. Beyond binary task success, HRC-SimBench introduces interpretable interaction-centric metrics spanning synchronization, responsiveness, protocol compliance, and safety. We evaluate adapted policies based on GR00T, π0.5, and ACT under a unified protocol. Results show that current foundation robot policies struggle substantially in collaborative settings despite strong manipulation ability, revealing major limitations in temporal coordination and intent-aware behavior. Fine-tuning on HRC-SimBench consistently improves collaborative performance. In a real-world adaptation study, simulation data generated by HRC-SimBench improves GR00T N1.5’s physical-task success rate from 0.10 to 0.43, demonstrating the benchmark’s value for advancing interaction-centric robot learning.