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LimX Dynamics Oli EDU Humanoid Robot
LimX Dynamics Oli EDU Humanoid Robot
LimX Dynamics Oli EDU Humanoid Robot
LimX Dynamics Oli EDU Humanoid Robot
LimX Dynamics Oli EDU Humanoid Robot

LimX Dynamics Oli EDU Humanoid Robot

SKU RBD-Lix-05
$72,000.00 - $72,000.00
$72,000.00
$72,000.00 - $72,000.00
$72,000.00
Availability:
in stock

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See It In Action

Frequently Asked Questions

What is the LimX Oli Edu?

The Oli Edu is a tabletop bipedal humanoid platform from LimX Dynamics built for university research and developer learning. It has 12 actuated joints, weighs about 8 kg (18 lb), stands 70 cm (27.6 inches) tall, and is designed for studying bipedal locomotion, balance recovery, and reinforcement learning policies in a safe, manageable form factor. It is not a full-sized service humanoid.

How is it different from the LimX TRON1?

TRON1 is a full-sized industrial bipedal platform standing roughly 1.4 m and weighing 60+ kg, designed for real-world locomotion research and field deployment. Oli Edu is the educational scale-down: same control philosophy, same SDK family, but a fraction of the size, weight, cost, and risk. Oli is the right starting point for labs new to bipedal research; TRON1 is the next step up once a team has proven a research direction.

What programming interface does it expose?

An open Python and C++ SDK plus ROS 2 Humble packages. The locomotion controller exposes joint-torque, joint-position, and high-level walk commands. URDF files for MuJoCo and Isaac Sim are included. LimX maintains an active GitHub with sample reinforcement-learning training scripts and a sim-to-real transfer pipeline.

Can students train their own walking policies?

Yes. The Oli Edu is specifically scoped for that workflow. The MuJoCo simulation environment matches the real robot's joint dynamics closely enough for sim-to-real transfer. Sample PPO and SAC training scripts are included. A standard pipeline: train in MuJoCo for a few million steps, validate on the simulated Oli, deploy to hardware. Most teams achieve robust walking within a semester.

How is it powered and how long does it run?

An onboard 5000 mAh lithium-polymer battery provides approximately 30 minutes of active walking or 1 hour of standing demonstrations per charge. Charging from empty takes 90 minutes. An AC tether is included for benchtop development so the battery is not consumed during code-iteration sessions.

What safety features prevent it from damaging itself or surroundings?

Joint-torque limits are set conservatively in the factory firmware. Compliance control absorbs collisions softly. A built-in fall-detect routine cuts joint power to prevent gear damage when the robot detects a fall. A physical kill switch on the back disables all motors instantly. For untethered tests, run on padded surfaces and supervise actively.

Does it ship with cameras or other sensors?

The Edu base configuration includes an IMU and joint encoders. Optional add-ons sold separately are an RGB camera (front-mounted), a depth camera, and a small lidar puck for floor mapping. The chassis has standardized mounting points and USB power outputs to support custom sensor additions by students.

Is this a single-user or classroom platform?

Sized for single-student or small-group use. A typical lab purchases 2 to 4 units for a class of 20 students rotating through hands-on sessions. Each robot ships with its own controller and SDK install media. Multiple robots can run in the same room without cross-talk because each pairs uniquely to its controller.

What is the warranty?

12-month manufacturer warranty covering motors, controllers, and electronics. The battery is warranted 6 months. Educational pricing and warranty service are coordinated through RoboticsSelect as the authorized LimX Dynamics dealer.

What is realistic to accomplish in one academic semester?

A motivated graduate student can typically accomplish: characterize the robot's joint dynamics in simulation, train a policy that walks forward on flat ground in sim, deploy that policy to hardware, and demonstrate basic disturbance rejection (push recovery). More advanced topics like uneven-terrain walking, language-conditioned commands, or multi-robot coordination usually take a full year or are dissertation-scale.