We present the NeuralNexus Arm, an open, low-cost 6-DOF robotic manipulator built by an undergraduate engineering team, together with the design decisions and debugging experience needed to reproduce it. The arm is driven by a single STM32H743 microcontroller on a custom printed circuit board (PCB) and combines two stepper-driver strategies on one controller: push-pull 3.3 V step/direction outputs for onboard TMC2209 drivers on the three wrist joints, and open-drain outputs for external CL57T and DM542 drivers on the three high-torque proximal joints. We describe the mechanical design, mixed-driver electronics, interrupt-driven firmware, a MATLAB/Simscape-based inverse-kinematics pipeline, a browser-native control interface using the Web Serial API, and a lightweight vision pipeline for object localisation and autonomous pick-and-place tasks. We also document non-obvious hardware and firmware failure modes encountered during the transition from a development board to the custom PCB as reproducibility guidance. All design files and firmware are released openly. The platform actuates all six axes under coordinated control at a 2 kHz update rate and executes both manual and pre-recorded motions from the browser interface.
Figures & tables
Robot
Cost ($)
DOF
Controller
Motor
Key Difference from NeuralNexus
HELENE
<1160
6
ESP32 + ROS Host
Stepper
Single closed-loop stepper driver family throughout (no mixed architecture); ROS-based control, not browser-native; the only competitor with a published ISO 9283 repeatability validation.
iArm
≈250
6
Raspberry Pi 4 + ROS
Serial Bus Servo
Servo-actuated, not stepper-driven; far lower torque/payload class; ROS-based host software rather than a zero-install browser interface.
EduSCARA
≈150
4
STM32 Nucleo + Python API
Hobby Servo
Only 4-DOF SCARA kinematics (RRPR), not a full 6-DOF articulated arm; potentiometer feedback rather than closed-loop stepper/encoder joints.
AR4 (MK3/MK4)
≈2000
6
Teensy 4.1 + Arduino Nano/Mega
Open-Loop Stepper
Uses one driver family only (DM542T/DM320T, same open-loop class at different current ratings) – not a mixed architecture; encoders exist but closed-loop correction is not implemented in firmware; Arduino/ROS control, no browser-native UI; non-commercial license restricts redistribution/resale.
PAROL6
≳350
6
Custom STM32 Board + Python Commander GUI
Stepper (Closed-Loop Upgradable)
Single driver family on its custom board (closed-loop is an optional FOC-driver upgrade, not a factory heterogeneous design); control is via a desktop Python app, not a browser; closest analog to our custom-PCB approach but without driver-family mixing.
Forte
<215
6
Off-the-Shelf Drivers, No Custom Board
Stepper (Capstan/Belt Drive)
No custom controller PCB at all – uses off-the-shelf drivers with a host PC; achieves low cost via a capstan-cable/belt drivetrain rather than gearbox+belt reductions; open-source release status unconfirmed.
TABLE I : Comparison of NeuralNexus with representative open-source robotic manipulators.
Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7
Fig. 2 : The assembled NeuralNexus Arm.
Fig. 3 : Custom STM32H743 controller PCB, showing the onboard TMC2209 drivers for the wrist joints and the terminals to the external closed-loop drivers.
Joint
Motor class
Driver
GPIO mode
J1 (base)
NEMA 23
DM542
Open-drain
J2 (shoulder)
NEMA 24
CL57T
Open-drain
J3 (elbow)
NEMA 24
CL57T
Open-drain
J4 (wrist 1)
NEMA 17
TMC2209 (onboard)
Push–pull
J5 (wrist 2)
NEMA 17
TMC2209 (onboard)
Push–pull
J6 (wrist 3)
NEMA 17
TMC2209 (onboard)
Push–pull
TABLE II : Per-joint actuator and driver mapping.
Fig. 4 : Controller architecture. A single STM32H743 drives two stepper-driver families over separate electrical interfaces: push–pull 3.3 V logic to the onboard TMC2209 wrist drivers, and open-drain, common-anode 5 V signalling to the opto-isolated external closed-loop drivers on the proximal joints. Per-joint AS5047P magnetic encoders return over SPI [ 15 , 19 , 17 , 16 , 20 ] .
Motor class
Joint(s)
Supply voltage
NEMA 17
J4–J6 (wrist)
12 V
NEMA 23
J1 (base)
24 V
NEMA 24
J2, J3 (shoulder, elbow)
48 V
5 V (opto)
J1–J3 driver inputs
5 V (buck from 12 V)
Logic (MCU)
—
3.3 V (LDO)
TABLE III : Motor-supply rails by motor class.
Joint
Translation (m)
Joint Axis
Type
J1
(0,0.017,0)
(0,0,−1)
Revolute
J2
(0,−0.075,−0.138)
(0,0,1)
Revolute
J3
(0.310,0,−0.024)
(0,0,1)
Revolute
J4
(−0.060,0.160,−0.051)
(0,0,1)
Revolute
J5
(0.041,0,−0.1065)
(0,0,1)
Revolute
J6
(0,0.110,−0.041)
(0,0,1)
Revolute
TABLE IV : Joint frame transformations extracted from the MATLAB rigidBodyTree model.
Fig. 5 : Kinematic modelling and motion execution workflow. The manipulator is modelled using a MATLAB rigidBodyTree , inverse kinematics generates the required joint angles, and the computed commands are transmitted to the STM32H743 controller for synchronized stepper motor actuation.
Fig. 6 : Browser-native control panel (Web Serial API).
Fig. 7 : Detected 8×5 inner-corner patterns from nine calibration images captured at different positions and orientations.
Fig. 8 : Detected object-processing stages used for target localisation.
Fig. 9 : Visualisation of planar homography calibration using the 32mm chessboard. The detected image-plane corner points are mapped to a top-down representation of the calibrated workspace.
Subsystem
Cost ($)
Actuation — motors, drivers, gripper
$300.38
Control, PCB & sensing electronics
$252.36
Vision compute
$178.80
Mechanical parts (gears, pulleys, belts, bearings)
$296.82
Local hardware — misc. components, fasteners
$149.96
Enclosure / structural fabrication
$293.08
TABLE V : Bill of materials summary by subsystem. Full itemised breakdown in Appendix Acknowledgment .
Fig. 10 : Selected frames from Video 2, showing a full pick-and-place sequence: https://youtube.com/shorts/Vg3pZLFrEdM .
Metric
Value
Positioning repeatability
±1.3 mm
Pick-and-place cycle time
12.5 s
Task success rate
97%
Maximum payload
2000 g
Maximum joint speed
10∘ /s
TABLE VI : Experimental Performance Summary
Item
Qty
Unit ($)
Total ($)
Supplier
Used in
Actuation — motors, drivers, gripper
NEMA 23 stepper motor (Local)
1
$22.65
$22.65
Local Shop
J1 (base)
NEMA 17 stepper motor (Local)
3
$7.45
$22.35
AliExpress
Wrist joints
NEMA 24 stepper motors CL57T drivers+ gearboxes + PSU (StepperOnline)
2
$119.20
$238.40
Stepper Online
J2, J3
DM542 stepper driver (Leadshine, DM542)
1
$9.83
$9.83
Local Shop
J1
Gripper / end-effector hardware (3D Printed)
—
—
$7.15
Local Shop
Gripper
TABLE VII : Detailed bill of materials. Costs converted from LKR at 1 LKR = $0.00298 (Aug. 2026).
Open-source mobile manipulators have reached 660(XLeRobot)buteverysub−1,000 platform shares three limitations: a fixed-height workspace, reactive-only control, and no protection against the stall-induced burn-out that destroys cheap Feetech servos. We present Nori Bot, a 17-DoF dual-arm mobile manipulator at $947 (~3% the cost of comparable commercial platforms) that addresses all three: (1) a 600mm Z-axis lift on the existing servo bus for floor-to-counter reach; (2) a thin-client Raspberry Pi 4 paired with the OpenClaw proactive agent runtime so cron jobs and hooks trigger physical tasks autonomously; and (3) a software safety stack with sensorless grip-force feedback via motor current on a soft TPU finger. Code, CAD, and the skill manifest will be released.
Antonio Li, Sungjoon Park, Wen Ni Chew
Department of Computer Science, Columbia University
We present a bimanual mobile manipulator built on the open-source XLeRobot with integrated onboard compute for less than $1300. Key contributions include: (1) optimized mechanical design maximizing stiffness-to-weight ratio, (2) a Tri-Bus power topology isolating compute from motor-induced voltage transients, and (3) embedded autonomy using NVIDIA Jetson Orin Nano for untethered operation. The platform enables teleoperation, autonomous SLAM navigation, and vision-based manipulation without external dependencies, providing a low-cost alternative for research and education in robotics and robot learning.
Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex "hold-and-manipulate" tasks that typically require bimanual coordination. However, existing devices are often hindered by bulky hardware that limits interaction to large objects and rely on explicit hand motion triggers that lack environmental context. To address these challenges, we present EgoThumb, a fully open-source, vision-driven supernumerary robotic thumb. EgoThumb features a compact, dual tendon-driven finger whose actuation is relocated to the forearm, reducing distal weight while enabling precise active joint control. The system utilizes a visual imitation learning policy that maps egocentric images from a palm-facing camera directly to continuous joint positions. By implicitly decoding human intent from visual cues capturing both the hand and the object of interest, EgoThumb achieves robust autonomous manipulation and strong generalization across diverse users and environments. All hardware designs and software implementations are fully open-sourced at https://ego-thumb.github.io.
Jianyu Hou, Jiawei Fu, Weikang Wan +3
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA. · Sudo AI GmbH, 8050 Zurich, Switzerland.