Replacing Expensive Servo Motors with AS5600 Magnetic Encoders: A Detailed Low-Cost DIY Robot Arm Guide

The Cost Dilemma for SO-101 Robot Arms and Robotics Hobbyists

The open-source robotics project SO-ARM series (including SO-100 and SO-101) has quickly gained popularity in the maker community, enabling individual hobbyists to build a robot arm at relatively low cost for learning, experimentation, and even machine learning training. The SO-ARM series (Standard Open Arm) is an open-source robot arm initiative driven by Hugging Face's LeRobot project. Its core goal is to lower the hardware barrier for Embodied AI research — traditional industrial robot arms cost tens of thousands of dollars, while the SO series compresses the BOM cost down to a few hundred dollars, making it possible for individual developers to train robot policies at home. SO-100 was the first generation, and SO-101 improved upon it with optimized structural design and better parts compatibility. However, for newcomers just getting started, one persistent pain point remains — cost.

Recently, a robotics newcomer posted a question on Reddit that showed real engineering thinking: they wanted to build an SO-101 but didn't want to spend too much on expensive encoder motors. So they conceived an alternative approach — using ordinary servos paired with AS5600 magnetic encoder chips and diametrically magnetized magnets to achieve closed-loop joint position feedback.

This idea may seem simple, but it touches on a core trade-off in low-cost robot design: how to replace expensive servo systems with a combination of cheap components without sacrificing too much precision.

How the AS5600 Magnetic Encoder Works and the Replacement Logic

Why Are Encoder Motors So Expensive?

The servos officially recommended for SO-101 (such as the Feetech STS3215 series) have built-in position feedback mechanisms that can precisely report the current angle of each joint. The STS3215 is a serial bus servo that uses a single-wire half-duplex TTL serial communication protocol, supporting multi-dimensional feedback including position, speed, and current. Unlike traditional PWM servos that can only receive control commands, bus servos allow the controller board to query the current angle, load, temperature, and other status information at any time, enabling true closed-loop control. The STS3215 has a position resolution of 4096 steps (12-bit) and a torque of approximately 17kg·cm — parameters that make it well-suited for desktop-scale robot arm applications.

This integrated design brings convenience, but the price rises accordingly. A single STS3215 costs between $15–25, and a 6-axis robot arm requires at least six, putting motor costs alone at $100–150 — often the largest chunk of the entire project budget.

How the AS5600 Enables Low-Cost Position Sensing

The AS5600 is a widely popular contactless magnetic rotary position sensor developed by Austrian semiconductor company ams-OSRAM. It's the entry-level model in their magnetic position sensor product line, aimed at consumer-grade applications. A single chip typically costs just a few cents to a couple of dollars.

Its working principle is based on the Hall effect: the chip integrates an array of Hall elements that detect the magnetic field vector direction produced by a diametrically magnetized magnet positioned above it. It calculates the magnet's absolute angle using a CORDIC algorithm. It outputs absolute angle information with 12-bit resolution (4096 positions) and supports I2C and analog/PWM output. The theoretical angular accuracy is approximately 0.088°, but actual accuracy is affected by magnet quality, air gap distance, temperature drift, and other factors, with typical error around ±1°.

The poster's modification concept leverages exactly this capability:

• Use ordinary cheap servos for actuation
• Install an AS5600 chip + diametric magnet at each joint
• Read the true joint angle via the AS5600 to build an external closed-loop feedback system

It's important to understand the fundamental significance of closed-loop control here. In open-loop control, after the controller sends a target angle command to the servo, it has no way of knowing whether the servo actually reached the target position — if external forces interfere, gears slip, or the load is too heavy, the actual position will deviate from expectations without the system being aware. Closed-loop control continuously measures the actual state through sensors, compares it with the target value to generate an error signal, and then uses control algorithms like PID to adjust output, achieving precise positioning. For teleoperation and trajectory replay scenarios in machine learning training, closed-loop feedback is especially critical — each joint angle of the leader arm needs to be read precisely to drive the follower arm to reproduce the same motion.

This way, you can bypass expensive integrated encoder motors and approximate the same functionality with a "cheap servo + cheap sensor" combination.

Engineering Challenges: From STL Modifications to Firmware Adaptation

Key Points for Mechanical Structure Modification

To implement this solution, you must first modify the existing STL 3D model files. The SO-101's joint structure is designed around the original motors, with no mounting positions reserved for AS5600 chips and magnets.

STL (Stereolithography) is the most commonly used 3D model file format in 3D printing, using triangular facets to approximate object surfaces. All structural parts of the SO-101 are designed for FDM (Fused Deposition Modeling) 3D printing, and users can directly download STL files to manufacture them on consumer-grade printers. However, the STL format itself contains no parametric design information, making direct editing of STL files extremely difficult. A more reasonable approach is to use CAD software like Fusion 360, SolidWorks, or the open-source FreeCAD to remodel the modified sections based on the original design intent, then export to STL for printing. Modifications must also account for 3D printing tolerances (typically ±0.2mm) and the effect of layer line orientation on structural strength.

For the magnetic encoder to work properly, the magnet must maintain precise coaxial alignment with the chip, with a gap controlled within 0.5–3mm. Specifically, you need to design a magnet seat at the end of the joint shaft and a chip mounting slot on the corresponding stationary structure. Any axial offset or tilt will significantly degrade reading accuracy.

Three Technical Challenges That Must Be Solved

From an engineering perspective, this solution must also overcome the following practical issues:

• Magnetic interference: The magnetic field generated by the servo's internal motor may interfere with AS5600 readings. If the sensor is too close to the motor, magnetic shielding or increased physical distance is needed to mitigate this.

• Mechanical backlash compensation: Ordinary servo gears have backlash. While an external encoder can measure the true angle, the control algorithm needs additional compensation for these nonlinear errors. Backlash refers to the dead-travel angle that occurs during reverse motion due to tiny gaps between gear tooth surfaces in a gear train. Cheap servos typically use plastic gear sets with backlash of 1°–3°, while high-end bus servos use metal gears and more precise manufacturing processes, keeping backlash under 0.5°. In robot arm applications, backlash from multiple joints creates a cumulative effect — if each joint has 2° of backlash, the position uncertainty at the 6-axis arm's end effector could reach centimeter-scale. Even if external encoders accurately measure joint angles, the transmission dead zone caused by backlash still makes the control response nonlinear, requiring dead-zone compensation in the control algorithm or more advanced model predictive control strategies.

• Control firmware rewrite: The official SO-101 control solution is written based on the original servo's feedback protocol. Switching to external AS5600 sensors requires writing your own firmware logic for angle reading and closed-loop control — a non-trivial amount of work. There's also an easily overlooked wiring issue: the AS5600's I2C address is fixed (0x36), meaning only one AS5600 can be connected to a single I2C bus. A 6-axis robot arm would need an I2C multiplexer (such as TCA9548A) or a dedicated I2C bus for each sensor, adding hardware wiring and software driver complexity. The upgraded AS5600L supports a programmable I2C address, but costs more accordingly.

Feasibility: Community Experience and Practical Considerations

From the general experience of the DIY robotics community, the "servo + external magnetic encoder" approach is far from outlandish. Many low-cost robot arm and gimbal projects have used similar architectures, and the AS5600 is a frequent choice in such modifications thanks to its ease of use and low price. The open-source motor control project SimpleFOC also extensively uses the AS5600 as an angle feedback sensor for brushless motors, accumulating substantial application experience and debugging documentation.

However, successfully transplanting this to the SO-101 still requires significant engineering effort, especially in CAD modeling and firmware development. For a user who describes themselves as a "robotics newcomer," this path may have a steeper learning curve than simply purchasing the standard kit — but for that very reason, the learning gains are also more substantial. From control theory and embedded programming to mechanical design, this modification project covers virtually all core knowledge areas of robotics engineering.

You might not have noticed, but the original post is still seeking practical case studies from the community and has not yet received mature, validated feedback. The low-cost alternative solution is theoretically sound, but how well it performs in practice still awaits more real-world test data.

Trade-off Advice for Low-Cost DIY Robot Arms

This poster's thinking embodies the most valuable spirit of the open-source hardware community — not settling for copying existing solutions, but actively seeking the optimal balance between cost and performance. Replacing expensive encoder motors with AS5600s is essentially a "decoupling" operation: separating actuation from sensing and implementing each with the cheapest possible components.

For budget-constrained beginners, this kind of exploration is worth encouraging. But it's also important to recognize clearly that the high price of integrated motors corresponds to higher integration, less debugging hassle, and better reliability. Whether it's worth taking on additional engineering complexity to save money depends on your goal — is it to get a working system up and running as quickly as possible, or to enjoy the tinkering process and deep learning experience?

If you're also considering a similar DIY robot modification project, consider starting with small experiments like modifying STL files and verifying AS5600 magnetic encoder alignment accuracy. Gradually build up experience before deciding whether to fully commit to the approach.

Key Takeaways