Joint Encoders and Torque Sensing in Humanoid Robots: How a Joint Knows Where It Is and How Hard It Pushes
Encoders and torque sensing: how a humanoid joint knows where it is
Every actuator story on this site ends at the same place: a motor and a transmission are blind. What turns them into a joint is the sensing stack bolted around them — typically two position encoders and some way of measuring torque. That stack decides whether a humanoid can balance, feel contact and work safely next to people, and it is quietly becoming one of the most contested supplier markets in the industry.
Sources: Stabilarity Hub (reference sensor suite); RLS d.o.o. product data; Intel Market Research, 2026.
One joint, three sensors
A humanoid joint module packs three sensing jobs into a housing the size of a fist. The motor-side encoder sits on the rotor and runs fast: it commutates the permanent-magnet motor and feeds the velocity loop, so update rate matters more than absolute accuracy — incremental optical or magnetic devices with 10,000-plus counts per revolution are typical. The output-side encoder sits after the gearbox and answers a different question: where is the limb, right now, including at power-on? That calls for an absolute encoder, because a robot that has to wiggle every joint through a homing routine before it can stand up is not a product.
Why not just divide the motor angle by the gear ratio? Because the transmission is not rigid. Under load, a harmonic drive’s flexspline winds up, gear teeth deflect and backlash opens, so the motor’s idea of the joint angle and the limb’s actual angle disagree — by exactly the amount the transmission is straining. A single motor-side encoder cannot see that error. Dual encoding fixes it, and as a bonus turns the transmission into a spring scale: if the stiffness k of the drivetrain is known, torque can be estimated as τ = k(θmotor/N − θoutput). The third sensing job, torque, then has four competing answers, which is where the architectures diverge.
Figure 1 · Labelled cross-section · The sensing stack in a rotary humanoid joint module
Illustration: humanoid.guide, after published joint-module layouts (Tesla AI Day 2022 rotary actuator; TQ-RoboDrive/RLS cobot joints). Schematic; proportions are indicative.
Magnetic, optical, capacitive, inductive
Four encoder technologies compete for those two slots per joint, and none wins everywhere. Magnetic encoders dominate the output side today because they are non-contact, tolerant of dust and shock, and forgiving of installation error. RLS, the Slovenian encoder maker owned by Renishaw, markets a three-product stack straight at humanoid builders: the AksIM-2 off-axis ring (up to 20-bit, ring diameters from 29 to 80 mm) for shoulders, hips, elbows and knees; the through-shaft Orbis (up to 14-bit) where cabling has to pass through the joint; and the 8 mm RM08 (up to 12-bit) for fingers and grippers. PAL Robotics has integrated RLS magnetic encoders in its humanoids for balance-critical joints, and Renishaw has built custom AksIM variants for TQ-RoboDrive’s ILM robot-joint motors under a 28 mm outer-diameter constraint.
The catch is that a magnetic encoder’s worst enemy — a stray magnetic field — is exactly what a frameless permanent-magnet motor produces a few millimetres away. That is the opening for the alternatives. Capacitive encoders reach around 19-bit resolution with update rates above 10 kHz and are immune to magnetic interference. Inductive encoders, such as Celera Motion’s IncOder line and FLUX’s rotary encoders, make the same immunity claim while staying lightweight and hollow-shafted, and are increasingly pitched at legged and surgical robots. Optical incremental encoders still offer the highest counts and sub-microsecond latency, but lose absolute position at power-off and dislike dust and thermal cycling — which keeps them on the motor side, if they are used at all.
Table 1 · Encoder technologies in humanoid joints
| Technology | Typical resolution | Strengths | Weaknesses | Typical slot |
|---|---|---|---|---|
| Magnetic (on/off-axis) | 12–20 bit | Non-contact, rugged, wide install tolerance, cheap at volume | Susceptible to stray magnetic fields near PM motors | Output encoder (dominant today) |
| Optical incremental | 10,000+ cpr | Highest counts, latency under 1 µs | No absolute position without homing; dust and temperature sensitivity | Motor-side encoder |
| Capacitive absolute | ~19 bit | Magnetic-field immune, >10 kHz update, absolute | Fewer suppliers, cost | Output encoder in EMI-dense joints |
| Inductive absolute | up to ~22 bit | EMI immune, lightweight, large hollow bores | Newer to humanoids; larger footprint at high spec | Output encoder, balance-critical joints |
Sources: Stabilarity Hub (resolutions, latency, interference); RLS product data (magnetic, 12–20-bit); Celera Motion and FLUX product announcements (inductive). “cpr” = counts per revolution.
Four ways to feel torque
Position sensing has converged; torque sensing has not. The cheapest method is to not have a sensor at all: in a quasi-direct-drive joint with a gear ratio below about 10:1, motor current is a usable proxy for output torque, with control resolution around 1–2 percent of rated torque. The physics only works at low ratios. Push the same trick through a 120:1 harmonic drive and a newton-metre of contact at the output shows up as roughly 8 mN·m at the motor — buried under friction that the ratio amplifies by four orders of magnitude. That is why current-based sensing belongs to the QDD camp (CubeMars’ AKE80-8 runs 8:1; RobStride’s 17 N·m modules pair the low ratio with dual encoders), and why high-ratio joints need hardware.
The hardware options are three. Deflection-based sensing reuses the dual encoders: measure the windup across a compliant element of known stiffness — a series elastic actuator makes the spring explicit, a harmonic drive’s flexspline provides one for free — and compute torque from the angle difference. Dedicated strain-gauge torque sensors bond gauges to a machined flexure between gearbox and limb and resolve better than 0.1 N·m, at the price of roughly 30 percent of a rotary actuator’s bill of materials, per HONPINE’s published cost breakdown. And at the wrists and ankles, where the robot meets the world, six-axis force/torque sensors from suppliers such as Bota Systems, FUTEK, ATI and Robotous measure all three forces and all three moments at once — market research puts their adoption at about 70 percent of new humanoid platforms, at five to eight times the price of a single-axis device.
Figure 2 · Animated · Dual-encoder torque estimation: the output lags the geared-down motor by the transmission windup
Illustration: humanoid.guide. Estimation principle τ = k(θmotor/N − θoutput) per Stabilarity Hub.
Table 2 · Four ways a humanoid joint measures torque
| Method | Extra hardware | Indicative performance | Where it fits |
|---|---|---|---|
| Motor current estimation | None | 1–2% of rated torque | QDD joints with ratios below ~10:1 (CubeMars, RobStride, most Chinese QDD modules) |
| Deflection via dual encoders | Second encoder (already fitted) | Depends on stiffness model; free torque signal | SEA joints and harmonic-drive joints with calibrated compliance |
| Strain-gauge joint torque sensor | Machined flexure + gauges | <0.1 N·m | High-ratio precision joints; ~30% of a rotary actuator’s cost |
| Six-axis force/torque sensor | Dedicated 6-axis unit | 3 forces + 3 moments | Wrists and ankles; ~70% of new platforms; 5–8× single-axis price |
Sources: Zane Robotics (current-based, QDD ratios); Stabilarity Hub (deflection, strain-gauge resolution); HONPINE via RoboticsTomorrow (cost share); Intel Market Research (6-axis adoption and premium).
What the robots actually ship
Tesla put its cards on the table at AI Day 2022: the rotary Optimus actuator slide listed dual encoders and an integrated torque sensor alongside the harmonic drive, and the linear units carry force sensors in series with the rod. Reconstructions of the current design describe every structural actuator as an integrated module — motor, reducer, encoder and driver board pre-assembled and calibrated as a unit. The QDD camp goes the other way: Unitree-style joints and the commercial modules from CubeMars and RobStride skip the torque sensor entirely and lean on low ratios plus current control, which is a large part of why their actuators undercut Western designs on price. In between sit the compliance-first designs — Apptronik has said publicly that it tested 30 actuator generations before settling on Apollo’s force-controlled linear architecture.
The supplier map is redrawing itself around that split. On encoders, RLS/Renishaw, Netzer, Celera Motion and FLUX are joined by the chip-level players whose magnetic sensor ICs sit inside cheaper integrated modules. On torque, the specialist force/torque houses — Bota Systems, ATI, FUTEK, Robotous — now compete with Chinese sensor makers such as Keli scaling for humanoid volume. Intel Market Research sizes torque sensors for humanoids at roughly $600 million in 2025, growing to a projected $10.5 billion by 2034 at a 44.3 percent CAGR, with Asia-Pacific already the largest supplying region. Even discounting forecast optimism, the direction is clear: sensing is where a meaningful share of the joint’s future bill of materials is moving.
Chart 1 · Torque sensors for humanoid robots: market size forecast, US$ billion
Source: Intel Market Research, “Torque Sensor for Humanoid Robot Market”, 2026. 2026 and 2034 are the analyst’s projections; treat single-firm forecasts as directional.
Go deeper: The Humanoid Actuation Report
249 pages on motors, reducers, screws, bearings, encoders and drives, the vendors behind every major platform, and the cost curves that decide adoption to 2036.
The Humanoid Actuation Report All joints & actuators articles All humanoid.guide reportsKeep reading · Joints & actuators
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FAQ
Why do humanoid joints need two encoders?
The motor-side encoder commutates the motor and closes the velocity loop; the output-side absolute encoder reports the true limb angle, including at power-on and under load, when transmission flex makes the motor’s geared-down angle wrong. The pair also enables deflection-based torque estimation from the angle difference.
What resolution do humanoid joint encoders have?
Output-side absolute magnetic encoders typically run 14 to 20 bits — 16,384 to about a million positions per revolution. Motor-side incremental encoders run 10,000-plus counts per revolution with very low latency. Fingers get by with less: miniature 8 mm encoders around 12 bits.
Can a robot sense force without a torque sensor?
Yes, two ways. Quasi-direct-drive joints with gear ratios under about 10:1 infer torque from motor current to within 1–2 percent of rating. Joints with a second encoder can estimate torque from transmission windup if the drivetrain stiffness is calibrated. High-ratio joints without either need a real sensor.
Why are magnetic encoders standard if motors disturb them?
They are cheap, rugged, non-contact and tolerant of misalignment, and encoder makers have improved stray-field immunity with shielding and differential sensing. Where the field environment is still too hostile, capacitive and inductive absolute encoders offer similar resolution with intrinsic immunity.
How much of a joint’s cost is sensing?
In a torque-sensor-equipped rotary actuator, HONPINE’s published breakdown puts the torque sensor alone at about 30 percent of unit cost — second only to the reducer. Encoders are far cheaper per piece, but a humanoid needs about 60 of them before the hands, and six-axis wrist and ankle sensors cost five to eight times a single-axis device.
Sources
- Stabilarity Hub — Proprioception and Internal State Estimation for Humanoid Robots (encoder resolutions and latencies, dual-encoder torque formula, sub-0.1 N·m strain gauges, 30+30 encoder reference suite)
- RLS — Magnetic encoders for humanoid robots (AksIM-2 20-bit and ring sizes, Orbis 14-bit, RM08 12-bit finger encoder)
- RLS — PAL Robotics integrates magnetic encoder technology
- The Robot Report — Renishaw and RLS help to drive a robot revolution (custom AksIM for TQ-RoboDrive ILM joints, 28 mm constraint, stray-field immunity work)
- Zane Robotics — Quasi-Direct Drive Joints: The Engineering Sweet Spot (sub-10:1 ratios, current-based force control, 1–2% torque resolution, 120:1 friction-isolation numbers, CubeMars AKE80-8)
- OpenELAB — RobStride02 QDD 17 N·m dual-encoder joint module
- Intel Market Research — Torque Sensor for Humanoid Robot Market, 2026–2034 ($600M 2025, $1.35B 2026E, $10.5B 2034E, 44.3% CAGR, 6-axis adoption and pricing)
- Bota Systems — Robot joint torque sensors
- FUTEK — Humanoid robot sensors
- The Robot Report — Celera Motion adds inductive angle encoder for robots
- Humanoid Robotics Technology — Encoders for a legged robot (FLUX inductive encoders, EMI immunity, selection trade-offs)
- Laifual — Tesla Optimus robot joints and actuators (integrated module with encoder per actuator; ~28 structural actuators)
- Brian D. Colwell — A Complete Review of Tesla’s Optimus Robot (AI Day 2022 rotary actuator: dual encoders and integrated torque sensor; linear actuator force sensors)
- HONPINE via RoboticsTomorrow — Linear vs rotary actuators cost breakdown (torque sensor ≈30% of rotary actuator cost)
- IEEE Spectrum — Apptronik’s Apollo (30 actuator generations tested; force-controlled design philosophy)
