Sensor
CurrentScan the perception stack first: mapping, vision, proximity, touch, and orientation.
Shared
101
One-off
906
Top adoption
IMU · 40 robots
Shared-stack-first browsing for sensor layers used across home and humanoid robots.
Quick orientation across all four component layers. The current layer is highlighted.
Scan the perception stack first: mapping, vision, proximity, touch, and orientation.
Shared
101
One-off
906
Top adoption
IMU · 40 robots
See which radios, apps, and protocols repeat across robot ecosystems.
Shared
53
One-off
282
Top adoption
Wi-Fi · 116 robots
Compare autonomy stacks, compute platforms, navigation brains, and branded intelligence layers.
Shared
2
One-off
352
Top adoption
Not Officially Disclosed · 2 robots
Browse speech interfaces, assistant integrations, and voice-control patterns without the fluff.
Shared
11
One-off
62
Top adoption
Amazon Alexa · 33 robots
Shared components stay in the main scan path; one-off entries stay bucketed until you actually need them.
Directory layer
Use the repeated sensor signals to narrow the field quickly, then open the single-use entries only when an exact vendor label matters.
Tracked
1007
Shared
101
One-off
906
30d active
727
Shared leaders
Fresh 30-day verification
Browse lens
Start with the shared stack. The long tail is mostly single-robot hardware fragments, so collapsing it keeps the browse path fast without hiding edge-case sensors.
Shared stack first
These are the reusable pieces that recur across multiple robots, so they do the heavy lifting for fast comparison before you dive into the edge cases.
101 entries
ANYmal D · Apollo +38 more
ANYmal D · Apollo +38 more
A2 · Agile ONE +20 more
A2 · Agile ONE +20 more
BellaBot · Deebot T90 Pro Omni +14 more
BellaBot · Deebot T90 Pro Omni +14 more
4NE-1 Mini · A2 +13 more
4NE-1 Mini · A2 +13 more
A2 Ultra · B2 +10 more
A2 Ultra · B2 +10 more
Aqua20 Pro Ultra Roller Complete · Astribot T1 +10 more
Aqua20 Pro Ultra Roller Complete · Astribot T1 +10 more
A2 Ultra · CyberDog 2 +10 more
A2 Ultra · CyberDog 2 +10 more
ADAM · Apollo +10 more
ADAM · Apollo +10 more
ANYmal D · DOBOT Atom +7 more
ANYmal D · DOBOT Atom +7 more
ADAM · Agile ONE +7 more
ADAM · Agile ONE +7 more
Agile ONE · CLOiD +6 more
Agile ONE · CLOiD +6 more
ASIMO · Digit +6 more
ASIMO · Digit +6 more
A2 · As2 +6 more
A2 · As2 +6 more
KeenMow K1 · Lawn Companion X25 +6 more
KeenMow K1 · Lawn Companion X25 +6 more
ASIMO · DRC-HUBO+ +5 more
ASIMO · DRC-HUBO+ +5 more
Cocomo · CyberDog 2 +5 more
Cocomo · CyberDog 2 +5 more
Alpha Mini · Hobbs W1 +4 more
Alpha Mini · Hobbs W1 +4 more
David · HIVA Haiwa +4 more
David · HIVA Haiwa +4 more
CyberOne · G1 +4 more
CyberOne · G1 +4 more
A3 AWD Pro · Automower 450X NERA +4 more
A3 AWD Pro · Automower 450X NERA +4 more
MagicBot Z1 · MagicDog +4 more
MagicBot Z1 · MagicDog +4 more
Luna · NEO +4 more
Luna · NEO +4 more
A2 Ultra · FF Futurist +4 more
A2 Ultra · FF Futurist +4 more
M16 Infinity · Qrevo Curv 2 Flow +3 more
M16 Infinity · Qrevo Curv 2 Flow +3 more
Ballie · Built-In Vacuum and Mop Robot BCRI3BX1 +3 more
Ballie · Built-In Vacuum and Mop Robot BCRI3BX1 +3 more
Apollo · Astribot S1 +3 more
Apollo · Astribot S1 +3 more
Agile ONE · FF Futurist +3 more
Agile ONE · FF Futurist +3 more
A3 AWD Pro · Automower 450X NERA +3 more
A3 AWD Pro · Automower 450X NERA +3 more
Alpha Mini · Ami +3 more
Alpha Mini · Ami +3 more
Freo X Ultra · M16 Infinity +2 more
Freo X Ultra · M16 Infinity +2 more
DR02 · Figure 03 +2 more
DR02 · Figure 03 +2 more
Alpha Mini · LOVOT +2 more
Alpha Mini · LOVOT +2 more
A2 Ultra · CyberDog 2 +2 more
A2 Ultra · CyberDog 2 +2 more
Alpha Mini · ASIMO +2 more
Alpha Mini · ASIMO +2 more
AquaSense X · MiPA +2 more
AquaSense X · MiPA +2 more
NAO6 · Sora 30 +1 more
NAO6 · Sora 30 +1 more
MagicDog · Quanta X2 +1 more
MagicDog · Quanta X2 +1 more
AquaSense X · CyberDog 2 +1 more
AquaSense X · CyberDog 2 +1 more
Deebot T90 Pro Omni · Deebot X12 OmniCyclone +1 more
Deebot T90 Pro Omni · Deebot X12 OmniCyclone +1 more
ADAM · MenteeBot +1 more
ADAM · MenteeBot +1 more
As2 · Booster T1 +1 more
As2 · Booster T1 +1 more
Coco 2 · MiPA +1 more
Coco 2 · MiPA +1 more
ASIMO · Built-In Vacuum and Mop Robot BCRI3BX1 +1 more
ASIMO · Built-In Vacuum and Mop Robot BCRI3BX1 +1 more
CyberDog 2 · ergoCub +1 more
CyberDog 2 · ergoCub +1 more
A2 · Digit +1 more
A2 · Digit +1 more
Lymow One Plus · M6 +1 more
Lymow One Plus · M6 +1 more
iSkim · Sora 30 +1 more
iSkim · Sora 30 +1 more
A2 · Asimov DIY Kit (Here Be Dragons Edition) +1 more
A2 · Asimov DIY Kit (Here Be Dragons Edition) +1 more
Ami · MiPA +1 more
Ami · MiPA +1 more
aibo (ERS-1000) · Astro +1 more
aibo (ERS-1000) · Astro +1 more
MagicDog · micro:bit PU Robot Kit +1 more
MagicDog · micro:bit PU Robot Kit +1 more
Loona · ROBOTIS OP3
Loona · ROBOTIS OP3
Loona · ROBOTIS OP3
Loona · ROBOTIS OP3
LiDAX Ultra 3000 AWD · RockMow X1 LiDAR
LiDAX Ultra 3000 AWD · RockMow X1 LiDAR
aibo (ERS-1000) · Mirokaï
aibo (ERS-1000) · Mirokaï
ElliQ 3 · R1
ElliQ 3 · R1
Lymow One Plus · ZERITH H1
Lymow One Plus · ZERITH H1
Flagship Soundwave Auto-Converting Robot · Lawn Companion X25
Flagship Soundwave Auto-Converting Robot · Lawn Companion X25
PUDU D5 Series · Wanda 2.0
PUDU D5 Series · Wanda 2.0
iCub · Poketomo
iCub · Poketomo
Moflin · Romi Lacatan
Moflin · Romi Lacatan
DRC-HUBO+ · SURENA IV
DRC-HUBO+ · SURENA IV
Lymow One Plus · S4
Lymow One Plus · S4
S3 · YUKA mini 2 1000H
S3 · YUKA mini 2 1000H
A2 Ultra · Unitree H2
A2 Ultra · Unitree H2
ROMO · ROMO P2
ROMO · ROMO P2
Roomba Combo j5+ · Roomba j9+
Roomba Combo j5+ · Roomba j9+
Flow 2 · K20+ Pro
Flow 2 · K20+ Pro
Roomba Combo j5+ · Roomba j9+
Roomba Combo j5+ · Roomba j9+
Deebot X12 OmniCyclone · M16 Infinity
Deebot X12 OmniCyclone · M16 Infinity
A2 · Expedition A3
A2 · Expedition A3
Kuri · Vbot SuperDog
Kuri · Vbot SuperDog
Abi · aibo (ERS-1000)
Abi · aibo (ERS-1000)
DRC-HUBO+ · SURENA IV
DRC-HUBO+ · SURENA IV
FF Futurist · FX Aegis
FF Futurist · FX Aegis
Ami · MiPA
Ami · MiPA
Moflin · Romi Lacatan
Moflin · Romi Lacatan
temi V3 · Ultramarine P1
temi V3 · Ultramarine P1
Astro · Spot
Astro · Spot
K36 · MiPA
K36 · MiPA
Astro · Spot
Astro · Spot
ASIMO · CyberDog 2
ASIMO · CyberDog 2
Robot Vacuum Omni E25 · Robot Vacuum Omni E28
Robot Vacuum Omni E25 · Robot Vacuum Omni E28
Robot Vacuum Omni E25 · Robot Vacuum Omni E28
Robot Vacuum Omni E25 · Robot Vacuum Omni E28
aibo (ERS-1000) · PARO
aibo (ERS-1000) · PARO
ELIXIS-W · PUDU D7
ELIXIS-W · PUDU D7
Dex · Jupiter
Dex · Jupiter
Grogu™ gitamini · KATA Friends
Grogu™ gitamini · KATA Friends
Miko 3 · Miko Mini
Miko 3 · Miko Mini
LOVOT · PARO
LOVOT · PARO
Abi · TM Xplore I
Abi · TM Xplore I
Lymow One Plus · M6
Lymow One Plus · M6
Ami · EVE
Ami · EVE
Deebot T90 Pro Omni · Deebot X12 OmniCyclone
Deebot T90 Pro Omni · Deebot X12 OmniCyclone
Miko 3 · Miko Mini
Miko 3 · Miko Mini
CyberDog 2 · Robot Vacuum Omni S2
CyberDog 2 · Robot Vacuum Omni S2
Deebot T90 Pro Omni · Deebot X8 Pro Omni
Deebot T90 Pro Omni · Deebot X8 Pro Omni
Astro · Spot
Astro · Spot
GoMate · Panther
GoMate · Panther
4NE-1 · 4NE-1 Mini
4NE-1 · 4NE-1 Mini
Qrevo Edge 2 Pro · Saros Z70
Qrevo Edge 2 Pro · Saros Z70
Single-use index
Keep the rare branded edge cases available without forcing the main browse path to slog through one-off shells row after row.
906 single-use entries
156 entries
Single-robot components kept off the main scan path
113 entries
Single-robot components kept off the main scan path
80 entries
Single-robot components kept off the main scan path
148 entries
Single-robot components kept off the main scan path
155 entries
Single-robot components kept off the main scan path
78 entries
Single-robot components kept off the main scan path
176 entries
Single-robot components kept off the main scan path
Sensors form the perception stack, the hardware layer that lets a robot detect distance, motion, edges, texture, orientation, and the difference between empty floor and a real obstacle. On ui44, this route is most useful when you are trying to predict navigation quality before you care about branding or marketing language. A robot that names LiDAR, depth sensing, stereo vision, or force feedback is telling you something concrete about how it sees the world. That still does not guarantee good behavior, because integration quality matters more than sensor count, but the sensor layer is often the fastest route into real capability. If a product claims strong autonomy while listing only minimal perception hardware, that tension is usually worth investigating on the robot page.
The ui44 database tracks 1007 sensor components used across 349 robots.
Sensors only become valuable when the robot can fuse them into one coherent model. A camera sees appearance, LiDAR sees geometry, an IMU tracks orientation, and tactile or cliff sensors confirm what happens near contact. The software layer has to reconcile conflicting inputs, filter noise, and decide which stream to trust in messy homes with mirrors, low light, rugs, cables, pets, and moving people. That is why two robots can list similar sensors and still behave very differently in edge cases.
Consumer robots started with simple bump sensors, infrared proximity, and cliff detection. That was enough to prevent obvious collisions, but not enough to create reliable maps or structured movement. As LiDAR, camera SLAM, and cheaper compute arrived, the sensor stack became the backbone of systematic navigation rather than a safety afterthought. The current cycle is defined by sensor fusion. Modern robots increasingly combine distance sensing, vision, inertial data, and local AI processing so the machine can both map efficiently and understand what it is seeing instead of treating every object as a generic obstacle.
What to check and what to watch for when comparing options
When comparing sensor stacks, start with the environment. Large open-plan homes benefit from strong mapping hardware. Dark rooms punish camera-heavy systems. Stairs make cliff sensing and depth coverage more important. Pets, cables, and toys favor richer close-range obstacle detection. Then ask how much redundancy exists. A single impressive sensor label is less reassuring than a balanced perception package with fallback paths when one input becomes unreliable.
Real homes stress sensors in ways demo rooms do not. Dust coats optics, sunlight saturates cameras, mirrors create phantom geometry, and floor transitions can confuse low-cost edge detection. Sensor maintenance matters too. A robot with good optics but poor access for cleaning may drift in real use. The safest buying habit is to read sensor claims as evidence, then validate them against the robot's layout, form factor, and real-world task on the product route.
The direction of travel is clear: solid-state depth sensing, better on-device object recognition, and more intelligent fusion between vision and range data. The most meaningful gains are not just about adding more sensors. They are about making the robot less brittle when the environment changes. That is why newer platforms increasingly pair stronger perception hardware with local AI accelerators and more detailed obstacle-classification models.
No. More sensors can widen coverage, but weak calibration or poor fusion can still create a bad robot. Use sensor count as a clue, then validate whether the robot seems designed to combine those inputs intelligently.
Because they solve related navigation problems in different ways. LiDAR is strong at geometry and mapping. Cameras help with object understanding and visual context. Many premium robots now combine both because each fills gaps left by the other.
Jump from this route to the robot page and check whether the same product story shows up in navigation, obstacle avoidance, form factor, and deployment notes. Strong sensor claims should echo throughout the full product profile.
It is a browse signal, not a quality score. Higher counts usually mean shared comparison anchors. Lower counts often mean proprietary or more signature-heavy technologies that need product context before they become meaningful.
Use the component layer for evidence, the robot page for context, and Compare for decisions. Shared labels do not automatically mean identical behavior.
Leave once the question becomes product fit instead of technology meaning. The component layer should narrow your attention, then hand you off to the product routes where price, form factor, deployment fit, and broader system design matter.