The race to build humanoid robots is moving beyond secretive corporate labs and into the hands of independent developers.

Singapore-based Menlo Research has unveiled a DIY version of its open-source humanoid robot, Asimov, aimed at hobbyists, researchers, and robotics enthusiasts.

Priced at around $15,000—close to the project’s estimated bill-of-materials cost—the kit reflects a broader push to make bipedal robotics more accessible.

Recently, a hobbyist created a life-size sci-fi droid replica using 3D printing and AI voice technology, showcasing affordable tools for interactive home robotics and automation.

Modular humanoid platform

Menlo Research’s open-source humanoid robot kit has a strong focus on modular engineering and simulation-driven robotics development.

The 3.93 feet (1.20 meter) tall humanoid weighs around 77 pounds (35 kilograms and features more than 25 degrees of freedom, offering builders a fully customizable research platform rather than a consumer-ready robot. Delivered completely unassembled, the system includes detailed manuals and instructional build videos aimed at developers and advanced hobbyists.

A major technical highlight is the robot’s modular architecture. Independent leg, arm, torso, and head sections connect through universal motor mounting fixtures, allowing users to swap or upgrade components without redesigning the entire platform. The approach reduces maintenance complexity while enabling rapid experimentation with new actuators and control systems, reports Humanoids Daily (HD).

The humanoid also incorporates a parallel Revolute-Spherical-Universal (RSU) ankle mechanism that provides two degrees of freedom for roll and pitch movement. The design improves torque distribution across the ankle joint and allows the robot to respond more naturally to uneven terrain and ground reaction forces during walking.

To simplify locomotion control, Asimov uses passive articulated toes rather than powered toe actuators. These non-actuated joints assist with the transition from stance to push-off, improving traction and balance while reducing computational overhead and mechanical complexity.

Most structural components are optimized for Multi Jet Fusion (MJF) 3D printing, enabling the production of strong, lightweight parts without relying on expensive CNC machining processes. This lowers manufacturing costs while making replacement and customization easier for developers, according to reports.

Realistic training system

Asimov’s software stack is built around a “Processor-in-the-Loop” (PIL) simulation approach that deliberately moves away from idealized robotics models. Instead of assuming clean, perfectly timed sensor data and deterministic physics, the training environment injects realistic operational imperfections to better mirror real-world conditions.

This includes simulated CANBus communication delays of up to 9 milliseconds, producing stale or out-of-sync control signals, as well as artificially generated sensor noise through an I2C emulation layer. These disturbances are designed to replicate the unpredictability and latency inherent in physical robot systems.

At the learning core, the system uses an Asymmetric Actor-Critic reinforcement learning framework. The “critic” network is granted access to privileged ground-truth simulation data, enabling accurate evaluation of state and reward signals. In contrast, the “actor” operates under constrained conditions, receiving only noisy, delayed sensor inputs similar to what onboard hardware would experience.

By training under this mismatch, the policy learns to tolerate uncertainty and partial observability. The result is zero-shot sim-to-real transfer, allowing the robot to walk forwards, backwards, and recover from external pushes directly on hardware without additional tuning or calibration, reports HD.

The kit isn’t inexpensive, with a target price of around $15,000. However, Asimov publishes a full bill of materials on its GitHub repository, allowing builders to source components independently and potentially reduce costs. According to Hackaday, while still a significant investment, it is considered far more accessible than earlier humanoid robotics systems that required millions in development funding.