# JAEGER

Humanoid whole-body control usually drives the upper and lower body with one policy, so large arm motions can disturb balance and the two tasks interfere during training. JAEGER splits the body into two independent controllers that share observations and rewards, initializes the upper body with supervised learning, then trains the whole body with reinforcement learning while supporting both root-velocity and joint-angle command modes. In simulation comparisons on the H1 and H1-2 platforms, JAEGER's upper-body joint tracking error is lower than HumanPlus, ExBody and OmniH2O, with pose-mode joint errors about 30% to 60% of those methods.

Humanoid whole-body control usually drives the upper and lower body with one policy, so large arm motions can disturb balance and the two tasks interfere during training. JAEGER splits the body into two independent controllers that share observations and rewards, initializes the upper body with supervised learning, then trains the whole body with reinforcement learning while supporting both root-velocity and joint-angle command modes. In simulation comparisons on the H1 and H1-2 platforms, JAEGER's upper-body joint tracking error is lower than HumanPlus, ExBody and OmniH2O, with pose-mode joint errors about 30% to 60% of those methods.