The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
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Maze Applied Reinforcement Learning Framework
Whole-body motion tracking for the Unitree G1 in about 3 minutes on one GPU.
- humanoid-robotics
- lafan1
- mjlab
- motion-tracking
Whole-body motion tracking for the Unitree G1 in under 3 minutes on average on one GPU: return 0.82 with 0/40 clips failed, held from 2.74 min (mean of 15 seeds, range 2.21-3.61, fine checkpoints), 11.87 it/s (world model + Muon + overlapped CUDA streams, mjlab)
- humanoid
- robot-learning
- supermimic
- world-model
Whole-body motion tracking for the Unitree G1 in under a minute. BlinkMimic's world-model trainer rebuilt in JAX, 3.7x faster on four H100s.
Transformer-SRU LiDAR navigation for Unitree G1: Isaac Lab training, MuJoCo validation, and ROS 2 deployment.