Motphys

MotrixLab · Imitation & Reinforcement Learning Framework

Train robot policies in simulation and deploy them to real robots, across body types from humanoids and quadrupeds to arms and dexterous hands, supporting tasks from locomotion to fine manipulation.

One framework, from basic locomotion to whole-body control

One training pipeline works across humanoids, quadrupeds, arms and dexterous hands — handling whole-body motions like rugged-terrain walking, backflips and wall flips, and manipulation tasks like grasping, cabinet opening and in-hand control.

Arm · Lift Cube

Franka

Policies trained in simulation, deployed straight to real robots

Backed by MotrixSim's physical accuracy, trained policies have been validated on real humanoids, quadrupeds and dexterous hands.

Sim · MotrixSim
Real
Humanoid Box Climbing

Sampling overlaps learning — faster training on the same hardware

Simulation sampling and policy learning overlap within the same cycle without waiting on each other, cutting end-to-end training time significantly.

Quadruped training case

3–10×

End-to-end speedup on same hardware

12s

Train one quadruped gait (measured)

202K

steps/s · 12,288 parallel envs

Test setup · RTX 4090 + Ryzen 9 9950X3D

As fast as 12s to train one quadruped gait (measured).

Layered architecture, environment decoupled from training

Training environments and training logic are split into layers, making the framework easy to extend.

Layered architecture

01

Interface Layer

TrainingInferenceVisualization
02

Algorithm Layer

Training AlgorithmsNetworksOptimizers
03

Environment Layer

Env ConfigEnvironmentReward
04

Physics Layer

MotrixSim · In-house Engine
MJCF ModelsPhysics EngineCollision Detection

Design advantages

Decoupled modules
Environment development fully separated from training logic
Flexible config
Layered configuration with runtime overrides
Extensible
Add new components easily via the registry system
Multi-backend
One environment runs on different simulation and training backends
Experiment-friendly
Configs can be saved and compared for reproducibility

Community vs Commercial

MotrixLab offers a free community edition for academic research and learning; the commercial edition is for commercial use and delivery.

COMMUNITY

CommunityFree

GitHub
  • Free & open — academic research & learning only (no commercial use)
  • Full framework
  • Locomotion training framework & sample projects
  • Community & docs support
  • Tracks public releases

COMMERCIAL

CommercialCommercial delivery

  • Commercial use, redistribution & embedding
  • Adds manipulation & imitation-learning frameworks
  • On-premise deployment & cloud service
  • Domestic CPU / GPU / NPU support
  • Official engineering support + SLA
  • Co-development & scene buildingContact us
  • Training & consulting
  • Contract maintenance, upgrades & renewal benefits

Items marked “Add-on” are purchased and priced separately on top of the commercial license; “Contact us” items are negotiated case by case. Students and faculty may apply for an academic license of the commercial edition based on their research.