aiqbench analysis
This article introduces HumanoidToolBench, a new benchmark specifically designed to evaluate humanoid robots' ability to select tools and coordinate manipulation with locomotion. By analyzing performance gaps between tool selection and task completion across simulation and real-world Unitree G1 deployments, it highlights critical challenges in advancing general-purpose robotic intelligence. The release of the ToolBook dataset provides essential training data for researchers aiming to improve policy robustness against unseen tools and irrelevant instructions.
HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution
arXiv:2610.02089v1 Announce Type: cross Abstract: As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable tool and coordinating manipulation and, when needed, locomotion to complete the task. Existing benchmarks do not jointly evaluate these capabilities on a humanoid. We introduce HumanoidToolBench, an 18-task benchmark spanning three scenarios, three execution levels, and two tool-set modes, together with ToolBook, a dataset of 3.1k demonstrations collected in simulation and on a real Unitree G1. Evaluation of seven policies in simulation and three on the real robot reveals substantial gaps between selecting a suitable tool and completing the task. Focused GR00T N1.7 probes show reduced selection accuracy on unseen tools and continued task execution under unrelated instructions. Code and data are available at https://snu-pi.github.io/HumanoidToolBench/.
Abstract:As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable tool and coordinating manipulation and, when needed, locomotion to complete the task. Existing benchmarks do not jointly evaluate these capabilities on a humanoid. We introduce HumanoidToolBench, an 18-task benchmark spanning three scenarios, three execution levels, and two tool-set modes, together with ToolBook, a dataset of 3.1k demonstrations collected in simulation and on a real Unitree G1. Evaluation of seven policies in simulation and three on the real robot reveals substantial gaps between selecting a suitable tool and completing the task. Focused GR00T N1.7 probes show reduced selection accuracy on unseen tools and continued task execution under unrelated instructions. Code and data are available at this https URL.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.02089 [cs.RO]
(or arXiv:2610.02089v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.02089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 228 words
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