HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

Hugging Face Daily Papers Papers

Summary

HumanoidToolBench 提出一个18项任务的基准,评估人形机器人从工具选择到移动执行的能力,并发布包含3.1k条演示的 ToolBook 数据集(仿真与真实 Unitree G1 采集),揭示了工具选择与任务成功执行之间的显著差距。

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/.
Original Article
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Paper page - HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

Source: https://huggingface.co/papers/2610.02089 Can humanoids choose the right tool and then actually use it?

We introduce HumanoidToolBench, an 18-task benchmark evaluating humanoid tool selection and execution across three scenarios, three execution levels, and two tool-set modes. We also introduce ToolBook, a dataset of 3.1k demonstrations collected in simulation and on a real Unitree G1.

Evaluating seven policies in simulation and three on the real robot reveals a substantial gap between selecting a suitable tool and successfully completing the task. Additional GR00T N1.7 probes examine generalization to unseen tools and behavior under unrelated instructions.

Explore the benchmark and demonstrations on our project page. We welcome feedback from the humanoid learning and tool-use communities!

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