HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration
HRIBench is a diagnostic benchmark for intent-aware human-robot collaboration, using structured scenario scripts to model agent roles, temporal dependencies, and coordination constraints. It defines three interaction roles—Instructor, Collaborator, and Intruder—across 13 tasks with over 650 evaluation episodes, introducing interpretable metrics like synchronization, responsiveness, protocol compliance, and safety. Evaluations show current foundation policies struggle in collaboration, but fine-tuning on HRIBench significantly improves performance.
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[Submitted on 5 Jul 2026]
Title:HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration
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Abstract: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 HRIBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable interaction scenarios. HRIBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions. Building on this abstraction, HRIBench 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, HRIBench introduces interpretable interaction-centric metrics spanning synchronization, responsiveness, protocol compliance, and safety. We evaluate adapted policies based on GR00T, pi0.5, 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 HRIBench consistently improves collaborative performance. In a real-world adaptation study, simulation data generated by HRIBench 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.
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.13056 [cs.RO]
(or arXiv:2607.13056v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.13056
arXiv-issued DOI via DataCite
Submission history
From: Chang Liu [view email] [v1] Sun, 5 Jul 2026 09:15:35 UTC (2,798 KB)
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