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Dexterous robotic hands combine information from cameras, joint encoders and fingertip sensors to find an object, make contact and adjust their grip. A camera can help locate an object before a grasp; a sensor at the fingertip can reveal where contact occurs and how it changes. A controller combines these signals with the hand’s configuration to move fingers or reposition an object. No single camera or sensor provides a complete understanding of the object.
How does a robot hand know where to grab an object?
Grasping involves two linked stages: approaching an object and responding to contact. An external camera can provide information about an object’s visible shape and pose, helping the robot plan an approach. Once fingers touch the object, fingertip sensing can provide information that an external view may not show directly, such as the location of contact or changes in force.
The controller can combine those contact estimates with joint-encoder readings, which describe finger configuration. This gives it a basis for adjusting finger motion or the object’s orientation. The exact sensors and control method vary by hand and task.
How do robotic fingers sense touch?
One approach uses a camera inside a deformable fingertip—not a camera pointed at a bare finger. In a 2020 prototype by Seung-hyun Choi and Kenji Tahara, a general USB camera sits inside a hollow, hemispherical silicone fingertip. Small colored markers embedded in the silicone shift as the surface deforms against an object. Image processing tracks their movement to estimate contact position and force-related information.
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The camera measures the marker movements; it does not directly sense pressure on its own. The system combines the estimated contact position with fingertip positions derived from hand kinematics to estimate and control the grasped object’s position and orientation.
For that research build, the authors report a 30-frame-per-second camera and brightness-adjustable LED illumination. They describe forming the silicone fingertip with a 3D-printed mold and designing it to be removable for repair. These are details of the prototype, not specifications or compatibility claims for a retail camera or a commercial robotic hand.
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What each sensor contributes
| Input | What it can tell the system | What it does not establish by itself |
|---|---|---|
| External vision | Visible object information before contact, including cues useful for locating an object and estimating its pose. | It does not necessarily reveal fingertip contact or force. |
| Camera-based tactile fingertip | In the Choi and Tahara prototype, marker motion through deformable silicone provides estimates of contact position and force-related information. | A camera alone is not a tactile sensor; the designed fingertip, markers and image processing are part of the sensing method. |
| Joint encoders | Finger or joint configuration, which can be combined with contact estimates for manipulation. | Configuration alone does not tell the controller everything about contact. |
| Force or tactile sensing | Contact-related information, such as location or changes in force; some systems also detect slip. | Capabilities depend on the particular sensor and system, rather than being guaranteed for every robotic hand. |
How can a robot stop an object from slipping?
A hand needs feedback after its fingers close. When tactile or force sensing detects a change in contact, a controller can use that information to adjust grip or move an object within the hand. Slip detection is one capability of some systems, not an automatic feature of every camera-equipped hand.
A 2024 study by Mao and colleagues in Nature Communications reports a multimodal tactile sensor with 0.05 mm/s slip sensing and a 4 ms response. Its system uses tactile-visual fusion, and the study demonstrates tasks including grasping a paper cup containing liquid and desktop sorting and cleaning. Those figures and demonstrations describe that study’s sensor and system; they are not standard performance specifications for robotic hands generally.
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What have experiments shown—and what are the limits?
In their 2020 contact-position experiment, Choi and Tahara report an average error of 1.475 mm, compared with a 60 mm fingertip-sensor diameter. This is a result for their prototype and experiment, not a general accuracy guarantee.
The authors also report improved pitch and yaw accuracy for their tactile-feedback method compared with the sensorless method in their experimental setup. They note mechanical limits on yaw motion, so the comparison does not mean sensing alone can overcome a hand’s physical constraints.
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The 2020 camera-based fingertip also depends on image processing. The authors identify ambient-light sensitivity and processing delay as practical considerations. Sensors can inform a controller, but estimation errors, processing time and the hand’s mechanics still affect what it can do.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should robotic hands be compared?
Finger count alone—or a headline sensor specification—does not describe how well a hand will perform a task. NIST’s draft framework for robotic-hand metrics identifies basic characteristics such as finger count, degrees of freedom and actuation, and argues for measures of task- and function-level capability so users can match a hand to a need.
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For sensing and manipulation, useful comparison questions include:
- What does the system sense: object pose, contact location, force, slip, or some combination?
- What are the sensing range and resolution, and how quickly does feedback reach the controller?
- How does the sensing perform under different lighting conditions?
- What do the hand’s mechanics allow it to do, including its degrees of freedom and actuation?
- Which tasks has the complete hand-and-controller system demonstrated?
The most meaningful evidence is performance on a task resembling the intended use, with the sensor, controller and hand considered together.
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