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RENFORCE DYNAMICS

// Precision Robotics ∩ Deep Reinforcement Learning


⚡ Real-Time Status & Impact

Our impact, live:

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💡 Core Focus: RL-Driven Motion

MISSION: To forge adaptive, efficient, and robust robotic motion solutions by mastering the fusion of AI and Physical Systems.

Domain Value Proposition
Deep RL Algorithmic excellence in dynamic, real-world robotic control.
Motion Control Optimized stability and real-time responsiveness for complex actuators.
System Integration Seamless transition from simulation-to-hardware (Sim2Real).

🚀 Join Our Mission

We are looking for brilliant minds to push the boundaries of RL and Robotics.

  • Open Roles: RL Intern, Robotics Engineer Intern, Full-Time Motion Planner.
  • Collaborate: Joint research, open-source contributions, and guest talks are welcome!

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  1. trackerLab trackerLab Public

    Unifying IsaacLab and Whole-Body Control in One Modular Framework

    Python 201 12

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