HumanoidToolBench gives researchers a way to test more than whether a humanoid can grasp an object: its 18 tasks assess tool choice and the full path to execution, including mobile actions. Paired with 3,100 demonstrations from simulation and a real Unitree G1, the benchmark exposes a practical gap: a robot can select a suitable tool yet still fail to finish the job.
What did the researchers build?
HumanoidToolBench is a benchmark for evaluating humanoid robots that need to choose tools and use them to complete tasks. The benchmark contains 18 tasks organized across three scenarios. Each task can be examined through three execution levels and two tool-set modes. The available description does not define those levels or modes in detail, but their inclusion lets evaluators examine tool use across different task and tool conditions rather than rely on a single demonstration.
The researchers also created ToolBook, a dataset with 3,100 demonstrations collected in simulation and on a real Unitree G1 humanoid. Together, the benchmark and dataset are intended to support both evaluation and the development of tool-using robot policies.
The central design choice is to treat tool use as a sequence of connected abilities. A robot must identify an appropriate tool, handle it, and carry out the task; if the task calls for a change of position, it must coordinate locomotion with manipulation. This makes the benchmark broader than a test of object recognition or grasping alone. It asks whether a humanoid can turn a tool choice into task completion.
What results did the benchmark reveal?
The evaluation covered seven policies in simulation and three policies on the real robot. Across those tests, the researchers found a substantial gap between choosing a suitable tool and completing the task. That distinction matters: a robot can make a plausible choice without being able to manipulate the tool effectively or finish the requested job.
The researchers also ran focused tests of GR00T N1.7. Its tool-selection accuracy fell when it encountered tools it had not seen before. In another probe, it continued executing even when given an instruction unrelated to the task. These findings point to two different challenges: generalizing tool choice beyond familiar examples, and checking that ongoing actions still match the instruction.
| Reported benchmark detail | Result |
|---|---|
| Tasks in HumanoidToolBench | 18 |
| Scenarios | 3 |
| Execution levels | 3 |
| Tool-set modes | 2 |
| ToolBook demonstrations | 3,100 |
| Policies evaluated in simulation | 7 |
| Policies evaluated on the real robot | 3 |
The available abstract does not report task-by-task success rates, selection-accuracy percentages, or comparisons between individual policies. The results therefore establish what kinds of gaps the benchmark surfaced, but not a numerical leaderboard or the size of each gap.
How does HumanoidToolBench work?
HumanoidToolBench evaluates tool use as more than a single yes-or-no capability. The benchmark spans three scenarios and varies both execution level and tool-set mode. Those dimensions provide a structure for testing whether a robot can handle tool-use tasks under different conditions. The abstract does not specify the exact scenarios, level definitions, or tool-set configurations, so their detailed meanings cannot be inferred from the reported summary.
The core workflow links selection to action. First, a policy must choose a tool that fits the task. Then the robot must carry out the necessary manipulation. Some tasks also require locomotion: the humanoid needs to move while coordinating its body and hands. This is important because a good tool choice is only an intermediate result. The test is whether that choice leads to task completion.
ToolBook complements this evaluation with 3,100 demonstrations gathered in simulation and on the Unitree G1. Demonstrations can provide examples for training or analysis, while running policies in simulation and on a real robot gives evaluators two settings in which to study behavior. The abstract does not say how the demonstrations are divided or which inputs and outputs they contain.
The evaluation also separates broad policy testing from targeted probes. Seven policies were tested in simulation and three on the real robot, while focused GR00T N1.7 tests examined responses to unseen tools and unrelated instructions. That design makes it possible to identify specific failure patterns alongside overall task performance, even though the abstract does not provide numerical scores.
Why does this matter for robotics?
For a humanoid intended to work outside a controlled demo, recognizing a tool is not enough. The robot has to select something appropriate, manipulate it safely and effectively, and—when necessary—move to carry out the job. A benchmark that measures only one of those stages can make readiness look stronger than it is. HumanoidToolBench directs attention to the handoff between a decision and successful physical work.
The reported findings also highlight issues that matter in changing environments. Tools may differ from examples encountered during training, and instructions may be ambiguous or inconsistent with the current action. The GR00T N1.7 probes show why evaluation should check both generalization to unfamiliar tools and whether a robot can stop or redirect when instructions do not fit.
For buyers and operations teams, the benchmark is a reminder to ask what a robot can complete—not just what it can recognize or select. Teams comparing platforms can browse humanoid robots on Robot Overflow, while treating tool-use results as one part of a broader assessment of reliability, safety, and deployment fit. The benchmark offers a way to frame those questions, not proof that any particular robot is ready for a specific workplace.
What are the limitations and open questions?
The available abstract gives the benchmark’s scale and its headline findings, but not enough detail to judge performance precisely. It does not report success rates, individual task results, policy-by-policy comparisons, or the size of the observed selection-to-completion gap. Those omissions prevent a direct assessment of how difficult each task was or which systems performed best.
The abstract also leaves the three scenarios, execution levels, and tool-set modes undefined. It does not describe how ToolBook demonstrations were collected, labeled, or split for training and evaluation. Finally, three real-robot policy evaluations provide useful hardware evidence, but the summary does not establish how results transfer to other humanoid platforms, tools, or workplaces. Further detail is needed to assess reproducibility and real-world readiness.
What are the most common questions about HumanoidToolBench?
What does HumanoidToolBench evaluate? It evaluates humanoid tool selection and task execution, including tasks where locomotion is part of completing the job.
What is ToolBook? ToolBook is a dataset of 3,100 demonstrations collected in simulation and on a real Unitree G1 humanoid.
Did the benchmark show that robots reliably complete tool-use tasks? No such general conclusion is reported in the available abstract. It reports a substantial gap between suitable tool selection and task completion.
What did the GR00T N1.7 probes test? They examined tool selection with unseen tools and whether the policy continued acting under an unrelated instruction. The reported findings were reduced selection accuracy on unseen tools and continued execution under unrelated instructions.
What should readers take away?
HumanoidToolBench makes tool use a more complete test: the robot must choose a tool and turn that choice into physical task completion, sometimes while moving. Its early findings show why selection alone is an incomplete measure of humanoid capability.