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What does dexterity mean in an evaluation?
For a practical evaluation, dexterity is demonstrated task performance: whether the hand completes a defined manipulation correctly, how long it takes, and how well it handles contact and variation. A hand with many joints is not necessarily more dexterous in a particular task; the result depends on its morphology, sensing, control, and the task setup.
The 2026 POMDAR benchmark, A Benchmark of Dexterity for Anthropomorphic Robotic Hands, takes a performance-based approach. It combines task correctness and execution speed into a throughput score. Report those underlying measures separately as well: a combined score alone can hide whether a hand is fast but error-prone or accurate but slow.
Which tasks should you use?
Use a small set of tasks that exercise different manipulation demands instead of relying on one impressive demonstration. POMDAR’s task configurations are a useful starting point:
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- Pure grasping: Test whether the hand can establish and maintain a grasp according to a stated success rule.
- Vertical manipulation: Assess manipulation in a vertical configuration.
- Horizontal manipulation: Assess manipulation in a horizontal configuration.
- Continuous rotation: Test whether the hand can keep manipulating an object through ongoing rotation rather than only reach a final pose.
These configurations probe different movement and grasping demands, so present results by task rather than collapsing them into a single demonstration. POMDAR uses mechanical scaffolding to constrain motions and reduce compensatory strategies, with the aim of making measurements less ambiguous and comparisons more reproducible. Scaffolding is part of the test conditions: disclose it, because a constrained fixture and an unconstrained real-world setup do not measure the same thing.
How do you make a task repeatable?
Write the protocol before running the comparison and apply the same rubric to every hand. For each task, specify:
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- Object and starting state: Identify the object and its initial position or configuration.
- Target state: Define the required end state in observable terms.
- Allowed strategy: State which contacts or grasp strategies are permitted, and describe any fixture or mechanical constraints.
- Success and partial completion: Define success before testing. If partial completion is useful, score it separately from failure rather than changing the rule after seeing results.
- Timeout and timing rule: State the timeout and when timing begins and ends. Apply the same rule across hands.
- Trials and exclusions: Report trial count, reset procedure, and how failed or excluded trials are handled. The cited benchmark papers motivate interpretable, standardized evaluation, but do not establish a universal trial count or one required real-world protocol.
What should you measure?
Keep the raw outcome measures visible. If you also publish a combined score, give its formula so readers can interpret it.
| Measure | What to report | What it helps show |
|---|---|---|
| Task correctness | Completions and errors under the stated success rule | Whether the hand achieved the target reliably |
| Execution time | Completion time and timeout handling | How quickly successful or attempted actions proceed |
| Task breadth | Results for each task configuration | Whether performance extends beyond one kind of grasp or movement |
| Contact and object state | Tactile or contact observations alongside kinematics and object outcomes, when measured | How contact, slip, or force regulation relates to the result |
| Robustness | Results under specified variations and the expected response to each | Whether performance persists or adapts when conditions change |
| Evidence setting | Simulation or physical hardware, sensors, fixtures, and control setup | What kind of evidence the result represents and how comparable it is |
How should you evaluate contact and robustness?
Capture contact when it affects the task
For tasks where contact placement, slip, or force regulation matters, success rate alone may not explain how the hand succeeded or failed. Record tactile evidence together with kinematics and object-state outcomes where those measurements are available. The 2026 TactiDex paper, A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation, describes a real-world benchmark organized around tactile-guided manipulation, with aligned tactile, kinematic, and object information and evaluation of manipulation success and physical realism.
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Vary conditions deliberately
Repeat tasks with controlled changes relevant to the manipulation, such as object pose or contact conditions. For each variation, state whether the correct action should stay the same or change in response. Bench2Dex uses these invariance and equivariance categories to organize perturbation tests in simulation. Its authors caution that simulated tactile signals do not replace measurements from physical sensors, so simulation results should not be presented as hardware validation.
How can you compare two hands fairly?
Compare results only when readers can see the conditions behind them. Report the hand morphology and sensing, object and fixture geometry, controller or policy, task setup, scoring rules, trial and reset details, and whether each result came from simulation or physical hardware. Differences in scaffolding, objects, sensing, or allowed compensatory motions can change a score; a single task result does not establish an overall dexterity ranking.
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Bench2Dex’s stated scope is simulation. Its 2026 benchmark covers 12 dexterous hands and 26 bimanual manipulation tasks; those are benchmark scope counts, not evidence about how common real-world robot hands are or how well they perform. POMDAR and TactiDex address different evaluation settings and task evidence, so their results should not be treated as directly comparable without matching conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do related resources establish?
RealDex, a 2024 resource titled Towards Human-like Grasping for Robotic Dexterous Hand, is relevant to human-like grasp motions, but it is not itself a standalone dexterity evaluation standard. More broadly, the benchmark papers described here offer useful task and measurement approaches; they do not establish one universal real-world protocol or a general statistic for humanoid-hand dexterity.
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