The Tool Desk
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What each sense tells a robot
A useful way to distinguish the three is to ask: Where is the object? What is happening at the contact? Where is the robot’s body or hand? These are explanatory questions, not exhaustive technical definitions. A robot’s sensors and software determine exactly what information it can estimate.
| Sensing modality | Information it can provide | Typical role in manipulation |
|---|---|---|
| Vision | The surrounding scene and objects, as seen by a camera or other visual sensor. | Locate objects and support scene understanding and planning before contact. |
| Touch | Information about interaction at contact points, including forces and surface properties. | Help assess grasp stability, recognize objects through contact, guide movement at a contact point, or regulate force. |
| Proprioception | The robot’s own configuration and movement. | Monitor the robot’s state as it moves and manipulates an object. |
Proprioception is distinct from touch: it concerns the robot’s own state, not simply what a tactile sensor at a fingertip or on a robot’s skin detects. Robot-sensing taxonomies treat proprioception, vision, tactile sensing, and force/torque sensing as separate modalities.
How the senses contribute over the course of a task
Manipulation is not just a matter of recognizing an object once. A robot must plan and act while its observations and the physical situation may change. One practical sequence shows how the modalities can complement one another:
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- Before contact: Visual observations can help locate an object and plan a reach or grasp.
- During the approach: Proprioceptive feedback can help track the robot’s own configuration and movement as it follows that plan.
- At contact: Tactile measurements provide information about the interaction at the points where the robot touches the object.
- While holding or moving: Contact information can support adjustments to force or motion, while proprioception continues to indicate the robot’s state.
This is a practical explanatory model, not a universal robot architecture. A system may use only some of these modalities, combine them differently, or rely on different sensor designs and control methods. Reviews of manipulation describe the task as integrating sensing and motor channels under uncertainty, with perception, grasp planning, execution, and goal-directed action unfolding over time.
What touch adds after contact
A camera can provide broad scene context, but tactile sensing addresses information available at the contact itself. That matters when a robot needs to respond to how an object is being held or manipulated rather than only where it appears in the scene.
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- Grasp stability: Contact measurements can help estimate whether a grasp is stable.
- Object recognition: Tactile information can contribute to recognizing an object through interaction.
- Tactile servoing: A robot can use contact feedback to guide motion in relation to a surface or object.
- Force control: Contact sensing can inform how the robot regulates the force it applies.
These are established application areas reviewed by Kappassov, Corrales, and Perdereau in their 2015 review of tactile sensing in dexterous robot hands; they do not mean every tactile sensor supports every task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing a sensing mix involves trade-offs
There is no single best combination or fusion method for every robot. The task, hardware, environment, and control approach shape which signals are useful and how they should be combined. Adding sensors can provide more information, but it also creates integration and deployment challenges.
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- Visual constraints: A robot’s view may be limited or occluded, which can make scene information harder to obtain.
- Tactile integration and durability: Tactile sensors must be incorporated into hardware such as robot hands and withstand the demands of use.
- Computation: Processing and combining sensor information has computational costs.
- Simulation-to-hardware transfer: A system that works in simulation may not transfer cleanly to physical hardware.
A 2026 systematic review by Ferdousee and Khan synthesized 19 studies and identified durability, computational cost, and sim-to-real transfer among ongoing challenges in robotic haptics. That study count describes the review’s corpus; it is not a general measure of sensor performance or a claim that every tactile sensor has the same limitations.
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Sources
- Annual Reviews (2019), “From Visual Understanding to Complex Object Manipulation”.
- Kappassov, Corrales, and Perdereau (2015), “Tactile sensing in dexterous robot hands — Review”.
- Ferdousee and Khan (2026), “Haptics in Robotics: A Systematic Literature Review”.
- “Sensing the Action: Rethinking Sensor Modalities and Multi-Modal Fusion in Vision–Language–Action Models for Robotic Manipulation” (2026).
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