Dual-critic humanoid loco-manipulation paper trains Unitree G1 policies in NVIDIA Isaac Lab
Evidence notes
- The paper compares unified and dual critic architectures for humanoid loco-manipulation reinforcement learning.
- Experiments train Unitree G1 policies through a 13-level curriculum in NVIDIA Isaac Lab.
Company context
NVIDIA is a compute and AI infrastructure company providing the simulation, training, edge-compute and model platforms used across modern robotics. Its stack includes tools for robot learning, digital twins, perception and on-device inference.