# How to Benchmark Humanoid Robot Tool Use: A Framework

> Learn how to benchmark humanoid robot tool use with stage-by-condition measures, repeatable trials, clear denominators and limits on what current research shows.

![How to Benchmark Humanoid Robot Tool Use: A Framework](https://humanoid-robots.nicheflash.com/media/articles/2741-a4qwme9qccvd-16x9.jpg)

- Source: https://humanoid-robots.nicheflash.com/blogs/how-to-benchmark-humanoid-robot-tool-use
- Author: [Humanoid Robots Editorial Team](https://humanoid-robots.nicheflash.com/authors/humanoid-robots-editorial-team)
- Publisher: Humanoid Robots
- Published: 2026-10-18
- Updated: 2026-10-18

To benchmark humanoid robot tool use, measure whether the robot selects a suitable tool, picks it up, makes the required contact and completes the task. Then report each stage under the conditions tested, such as stationary or mobile execution.

## Key takeaways

- A final task-success score can hide whether a failure came from tool selection, pickup, contact or task execution.
- Define success criteria for each applicable stage before running trials, and report each result with its denominator.
- Treat stationary and mobile execution as separate conditions that can affect several stages, not as stages in a single sequence.
- HumanoidToolBench tests three scenarios and three execution levels, but its real-robot evidence covers only a limited subset.[1]

## What should a humanoid robot tool-use benchmark measure?

A tool-use benchmark evaluates how a robot selects and uses tools to complete tasks. A practical framework distinguishes four process stages:

- **Selection:** Did the robot choose a tool with the property the task requires, including when decoys were available?
- **Pickup:** Did it acquire the selected tool?
- **Contact:** Did the tool make the required contact with the target?
- **Task outcome:** Did the intended result occur?

Stationary and mobile execution are conditions, not additional process stages. Test the same applicable stages under each condition, and record which conditions require movement while holding or using the tool. Mark a stage “not applicable” when the task does not require it; do not count it as a pass.

These measures are a diagnostic framework, not a universal scoring standard. They address tool-use capability, not fleet reliability, safety certification or commercial readiness.

## How does HumanoidToolBench test tool selection and use?

HumanoidToolBench evaluates 18 tasks across three scenarios, three execution levels and two tool-set modes, according to its authors.[1]

These tests distinguish functional selection from visual matching.

For BallRetrieve, for example, a robot should not earn full credit merely for choosing an object that looks like a tool. The evaluation should record whether it chose a suitable hook, acquired it, hooked and pulled the target, and completed the task. Those same applicable stages can then be measured under stationary and mobile conditions.

## How can you turn the framework into a useful scorecard?

Use a repeatable protocol so another evaluator can interpret or reproduce the results:

1. **Define the task and conditions.** State the task, tool-set mode, whether decoys are present, and whether execution is stationary or mobile. Keep results separate by task and condition.
2. **Write pass criteria in advance.** Define observable success for each applicable stage. For BallRetrieve, specify what counts as a suitable hook, successful acquisition, required hook-and-pull contact, and completion of the target outcome.
3. **Fix the trial unit.** State what counts as one attempt, including the starting state and when an attempt ends. Use the same rules across compared systems or conditions.
4. **Record outcomes and failures.** For every attempt, mark each applicable stage pass, fail or not applicable. Record the first failure and brief evidence notes, such as selecting a straight-stick decoy or dropping the tool.
5. **Report denominators.** For each stage, report successes divided by the number of attempts in which that stage was applicable and evaluated. Also report overall task completions divided by all valid attempts. If a stage score uses a conditional denominator, such as pickup trials only after successful selection, label it clearly.

Do not combine conditions into one score unless the aggregation rule is stated. If a paper reports only task success, do not infer separate pickup or contact results from it; identify the missing stage-level evidence instead.

**Compact scorecard template**

**Record one row per task and execution condition**

| Task | Condition | Attempts | Selection | Pickup | Contact | Task outcome | Evidence notes |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Task name | Stationary or mobile | Valid attempts: ___ | Passes / applicable attempts | Passes / applicable attempts | Passes / applicable attempts | Completions / valid attempts | Decoys, failure point, observations |

**Illustrative BallRetrieve entry—not a benchmark result:** Task: BallRetrieve; condition: stationary; attempts: 1; selection: pass (1/1, chose the suitable hook); pickup: pass (1/1); contact: fail (0/1, did not hook and pull the target); task outcome: fail (0/1); evidence: failure occurred at contact. A separate row would record mobile execution. A real evaluation should use its actual trial counts and observations.

## What does the study establish—and what remains uncertain?

The HumanoidToolBench study reports that ToolBook contains 3,094 demonstrations: 3,003 from simulation and 91 collected using a real Unitree G1. The authors also report evaluating seven policies in simulation and three on the real robot.[1]

The authors report gaps between selecting a suitable tool and completing the task.[1] This finding describes the policies and evaluations in the study; it does not establish that every humanoid system has the same gap. The real-robot demonstrations and evaluations are a limited subset, not comprehensive hardware validation.

HumanoidBench offers broader context: its authors describe challenging whole-body manipulation and locomotion tasks, rather than a benchmark focused specifically on tool selection through mobile tool execution.[2] Broad locomotion and manipulation coverage does not, by itself, show whether a robot can identify and use the right tool.

For product and operations teams, benchmark results are screening evidence, not proof that a robot is ready for a particular worksite. Validate the tools, objects and operating conditions that matter in the intended setting.

## How should teams use tool-use benchmark results?

- **Researchers:** State the tasks, conditions, tool-set modes, denominators and whether results came from simulation or a physical robot.
- **Engineers:** Check that decoys differ in a property relevant to the task, not merely in appearance.
- **Product and operations teams:** Treat benchmark performance as a screening signal and test the tools and conditions relevant to your setting.
- **Investors and observers:** Distinguish research results from evidence of repeatable commercial performance.

A useful benchmark reveals where tool use breaks down. A single task-success result cannot show whether the robot chose the wrong tool or failed during pickup, contact or task execution.

## Frequently asked questions

### Does success in a simulated tool-use benchmark prove a humanoid is ready for commercial deployment?

No. Simulation results show performance under the benchmark’s simulated conditions. They do not establish performance with a company’s tools, tasks or operating conditions. Physical-robot validation is still needed.

### How should teams compare tool-use results across robots with different bodies or hands?

Compare the tasks, tool properties, conditions and stage-level outcomes. Avoid treating an aggregate score as a direct comparison when the tested conditions differ.

### What should a benchmark include to test whether a robot can use tools it has not seen before?

As a proposed evaluation, separate familiar-tool results from tests with held-out tools, and state how those tools differ in task-relevant properties. Do not describe a benchmark as testing unseen tools unless its protocol establishes that distinction.

### How can tool wear or damage be incorporated into future humanoid tool-use evaluations?

Future evaluations could document tool condition and test defined wear or damage conditions, reporting those outcomes separately. The cited benchmark details do not establish a tool-wear protocol.

## References

1. HumanoidToolBench. Benchmark study describing the tasks, ToolBook demonstrations and policy evaluations cited in this article.
2. HumanoidBench. Benchmark for whole-body manipulation and locomotion tasks cited for context in this article.

## References

1. [arxiv.org](https://arxiv.org/abs/2610.02089)
2. [arxiv.org](https://arxiv.org/abs/2403.10506)