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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Dexterous robotic hands use cameras to locate objects and plan an approach, then use fingertip, force, and joint sensors to respond as fingers make contact. A controller combines those measurements to adjust a grasp or move an object within the hand. The exact setup varies: a camera by itself does not feel pressure, and no single sensor provides a complete picture of an object or grasp.
How does a robot hand know where to grab an object?
Before contact, an external camera can help a robot locate an object and estimate its visible shape or pose. That information helps the hand choose where and how to approach. Once fingers touch the object, fingertip sensors can report details that an outside view may not reveal directly, such as where contact occurs or how it changes.
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The controller can combine those contact estimates with joint-encoder readings, which indicate finger configuration. Using both, it can adjust finger motion or the object’s orientation. This is a general model, not a fixed recipe: the sensors, control method, and mechanical capabilities differ among hands and tasks.
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How do robotic fingers sense touch?
Some fingertips use tactile sensors that convert deformation or force at the contact surface into measurements. In one published design, a small camera sits inside a hollow, hemispherical fingertip made of soft silicone. Colored markers embedded in the silicone shift as the fingertip deforms against an object. Image processing tracks the markers to estimate contact position and force-related information.
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The camera is useful because it views changes inside a specially designed, deformable fingertip. It does not directly measure pressure simply by being a camera. The controller combines the estimated contact position with fingertip positions calculated from the hand’s kinematics to estimate and control the grasped object’s position and orientation.
A research prototype with a camera inside the fingertip
Choi and Tahara’s 2020 prototype used a general USB camera, brightness-adjustable LED illumination, and a 3D-printed mold to form the silicone fingertip. The fingertip was designed to be removable for repair. Those are details of that research build, not a standard product specification or a guarantee that a retail USB endoscope camera with LED will work with another hand.
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In the authors’ contact-position experiment, the prototype had a reported average error of 1.475 mm; the paper compares that result with the sensor’s 60 mm fingertip diameter. It is a result for that sensor and experiment, not a general accuracy figure for robotic hands. The paper also reports better pitch and yaw accuracy with its tactile-feedback method than with the compared sensorless method in its experimental setup, while noting mechanical limits on yaw motion.
What each sensor contributes
| Sensor or input | What it can contribute | What it does not establish by itself |
|---|---|---|
| External camera | Visual information to locate an object, estimate visible shape or pose, and observe orientation. | Pressure or contact details hidden from the camera’s view. |
| Camera inside a designed soft fingertip | Images of markers moving with fingertip deformation; processing can estimate contact position and force-related information. | A direct pressure reading independent of the fingertip design and image processing. |
| Joint encoders | Finger or fingertip configuration for combining with contact estimates in manipulation control. | The contact location or force at the object on their own. |
| Force or tactile sensors | Measurements used to estimate contact force and location; changes can inform grip adjustments or in-hand movement. | A complete understanding of the object or task without other measurements and control. |
How does a robot stop an object from slipping?
A hand can use changing tactile or force measurements to detect changes at the contact and adjust its grip. Slip sensing is one capability explored in recent multimodal systems, but performance depends on the specific sensor and controller rather than being an automatic property of every robotic hand.
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Mao and colleagues’ 2024 study reports 0.05 mm/s slip sensing and a 4 ms response for its multimodal tactile sensor. Their system combines tactile and visual information and was demonstrated grasping a paper cup containing liquid and performing desktop sorting and cleaning. These figures and demonstrations describe that study’s system; they are not standard specifications for robotic hands generally.
What are the limits of camera-based tactile sensing?
In the 2020 prototype, image processing is part of the sensing method, so illumination and processing time matter. The authors identify sensitivity to ambient light and processing delay as practical considerations. More broadly, sensor feedback can help a controller respond to contact, but it cannot remove estimation error or overcome the hand’s mechanical limits.
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How should robotic hands be compared?
Finger count alone does not show what a hand can do. NIST’s draft framework identifies mechanical traits such as finger count, degrees of freedom, and actuation, and emphasizes task- and function-level performance for matching a hand to a need. For sensor-enabled manipulation, useful comparison questions include:
- What does the hand sense: object pose, contact location, force, slip, or some combination?
- What sensing range and resolution are reported, and under what test conditions?
- How quickly does the system respond, and does the figure describe sensing alone or the broader control system?
- How robust is sensing to lighting and other environmental conditions?
- What can the hand’s mechanics do, including its degrees of freedom and movement limits?
- Which tasks were actually demonstrated, and how closely do they match the intended use?
NIST’s document is a draft performance-metrics framework, not a universal ranking of hands. The key is to compare sensor capability and demonstrated task performance alongside mechanical design.
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Sources
- Choi and Tahara, “Dexterous object manipulation by a multi-fingered robotic hand with visual-tactile fingertip sensors”, ROBOMECH Journal, 12 March 2020.
- Falco, Van Wyk, and Messina, “Performance Metrics and Test Methods for Robotic Hands (Draft)”, NIST SP 1227 Draft, 31 October 2018; NIST page updated 7 May 2026.
- Mao et al., “Multimodal tactile sensing fused with vision for dexterous robotic housekeeping”, Nature Communications, 11 August 2024.
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