HumanoidToolBench
Humanoid robot policies pick a suitable tool and use it, walking when needed, across 18 tasks in simulation and on a real Unitree G1.
It had 30 Hugging Face paper upvotes and 22 GitHub stars.
In the authors’ words
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/.
- Level
- Everyday Most adults, without training
- Scored
- Settled by events Predictions are scored against what later happens.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for HumanoidToolBench yet.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
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