HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution
Kyochul Jang, Seohyeon Park, Ohchul Kwon, Sangjun Park, Junhyeok Choi, Seungyeop Yi, Chaeyun Kim, Sangkyu Lee, Idan Szpektor, Avi Caciularu, Jongmin Park, Youngjae Yu · N/A · 2026
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Summary
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.
Abstract Summary
Key Points
- 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.
HumanoidToolBench
HumanoidToolBench evaluates whether humanoid policies select tools suited to the environment and the user’s instruction, then use them to complete the requested task ( HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution ). We instantiate the hierarchical levels in Table I as execution levels , which increase the demands of tool use.
Each task is a combination of scenario, execution level, and tool-set mode, yielding 18 tasks from three scenarios, three execution levels, and two modes.
Experiments
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Sources and Demonstrations
Figures are reproduced from Jang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.
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