Evaluate a humanoid robot hand by what it can reliably do, not by how many fingers or joints it has. Use repeatable tasks with stated success rules; report correctness and completion time separately; test different kinds of manipulation; and, when contact matters, record tactile and object-state evidence. A score is meaningful only alongside the hand, sensors, task setup, and test conditions that produced it.
What does dexterity mean in a robot hand?
For evaluation, dexterity is best treated as observable task performance: whether the hand completes a defined manipulation task, how quickly it does so, and how reliably it handles relevant changes in conditions. Finger count or nominal joint count describes hardware, but neither on its own shows what the hand can accomplish.
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POMDAR, a 2026 benchmark paper, takes a performance-based approach and combines task correctness with execution speed in a throughput score. That combined figure is useful for a summary, but the underlying correctness and time results should also be reported: a fast but error-prone hand and a slower, more accurate one have different strengths.
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What tasks should an evaluation include?
Use a set of tasks that tests different manipulation demands rather than relying on one successful demonstration. POMDAR’s task designs cover four configurations:
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| Configuration | What to assess |
|---|---|
| Vertical manipulation | Completion and timing in the benchmark’s vertical configuration. |
| Horizontal manipulation | Completion and timing in the benchmark’s horizontal configuration. |
| Continuous rotation | Whether the hand can sustain the specified rotational manipulation and meet the task’s success rule. |
| Pure grasping | Whether the hand achieves the defined grasping outcome. |
These are configuration labels, not a universal task list for every application. Select tasks that reflect the intended work, and state the object, start and target states, and required outcome for each one.
Make the task hard to misinterpret
POMDAR uses mechanical scaffolding to constrain motions and reduce compensatory strategies, with the aim of making measurements less ambiguous and comparisons more reproducible. A fixture can improve repeatability, but it also becomes part of the test: disclose its geometry and constraints, and do not imply that performance in a constrained setup automatically predicts performance in an unconstrained environment.
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How should you define success and measure performance?
Write the rubric before running trials
For each task, document the object, initial state, target state, permitted contact or grasp strategy, timeout, and what counts as completion. Keep the rubric unchanged when comparing hands. If partial completion is useful, record it as its own outcome rather than silently treating it as success.
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- Correctness: state the number of successful trials and the total number of trials included, along with the success rule. A success rate can be calculated as successful trials divided by included trials; explain how excluded or aborted trials were handled.
- Speed: report completion time and explain how timeout cases are represented. Make clear whether a time summary covers only successful trials or all attempts.
- Combined score: if using a throughput measure, give its formula and inputs. POMDAR combines correctness and execution speed, but a reader needs the formula to interpret or reproduce a score.
Do not let a single aggregate hide the trade-off between accuracy and speed. Keep trial counts and the treatment of failures visible beside any summary score.
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When should tactile and object-state evidence be included?
For tasks where slip, contact placement, or force regulation affects the outcome, completion alone may not explain how the hand succeeded or failed. Record contact evidence together with hand kinematics and the object’s state so that the physical interaction can be interpreted alongside the final result.
TactiDex, a 2026 real-world tactile-guided benchmark paper, aligns whole-hand tactile signals with kinematic and object-state information and evaluates manipulation success and physical realism. Its approach is relevant when tactile guidance is part of the task; it does not mean that every dexterity evaluation must use the same sensors or metric. State what sensing was available and what information was recorded.
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How can you test robustness?
Repeat tasks with controlled changes in object pose, contact conditions, or other disturbances relevant to the intended use. Define the expected response for each change: should the correct action stay the same, or should it change in a predictable way? Then report performance across those conditions rather than only under a nominal setup.
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What should a hand comparison disclose?
Two scores are interpretable side by side only when readers can see how each was produced. Report the setup and evidence setting with the results:
- Hand morphology and sensing setup.
- Object and fixture geometry, initial conditions, and task configuration.
- Controller or policy used.
- Success rubric, timeout, scoring formula, and trial count.
- Reset procedure and the handling of excluded, failed, or aborted trials.
- Whether results come from simulation or physical hardware, including relevant sensor and fixture details.
Benchmark scores depend on task design, scaffolding, object set, sensing, and allowed compensations. A ranking based on one task score without those conditions can confuse differences in setup with differences in hand capability.
What do the current benchmarks establish?
The 2026 papers offer complementary evaluation approaches rather than one universal protocol. POMDAR emphasizes structured tasks and correctness-plus-speed scoring; TactiDex focuses on real-world tactile-guided manipulation and aligned contact, kinematic, and object information; Bench2Dex studies visuo-tactile bimanual manipulation and perturbation robustness in simulation. RealDex (2024) is a dataset resource relevant to human-like grasp motions, not a standalone dexterity evaluation standard.
Bench2Dex describes a simulation benchmark spanning 12 dexterous hands and 26 bimanual manipulation tasks. Those figures describe the benchmark’s scope; they are not statistics about how common robot hands are or how well physical hands perform. The benchmark papers are research preprints, and their described methods do not establish a universally required trial count or a single standardized real-world protocol.
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