RL Policy Trained in 78 Seconds Transfers Successfully to Mujoco
yacineMTB · x · 2026-08-30
The author demonstrates a reinforcement learning policy trained on a custom simulator (dingsim), exhibiting movement behavior that appears to exploit the environment. Crucially, this behavior transfers successfully to the Mujoco CPU environment, validating the sim2real pipeline. The author notes the training was completed in just 78 seconds.
Related event: RL Locomotion Policy Trained in 78 Seconds on Custom Simulator(2 posts)→
More from Embodied
- SpaceX Removes 12.1 Tons of Debris from Boca Chica Beach — DimaZeniuk · 2026-09-01
- Chinese Humanoid Robots Dominate IFA: UBTECH, Unitree, and More — CyberRobooo · 2026-09-01
- Microduck Training: From Collision Avoidance to Fortnite Dances — tristanbob · 2026-09-01
- DLSS 5 Neural Rendering Successfully Ported to Half-Life 2 — ssh4net · 2026-09-01
- Backyard Cleaning Robot: The Future of Self-Maintaining Homes — CurieuxExplorer · 2026-09-01
- Humanoid Robot in the City: Police Escort and the Future of Robotics — CurieuxExplorer · 2026-09-01