Model Releases
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 req
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/.
Related
- Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
- UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
- CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
Source: arXiv cs.AI | 2026-10-02