Safe whole-body loco-manipulation paper validates model-learning control on Unitree Go2
Evidence notes
- The paper combines model-based admittance control for manipulation with a reinforcement-learning policy for legged locomotion.
- The authors validate the controller in simulation and hardware using a Unitree Go2 quadruped with a 6-DoF arm and wrist-mounted force-torque sensor.
Company context
Develops quadruped and humanoid robots, with strength in dynamic locomotion, vertically integrated hardware, and relatively low-cost commercial deployment. Unitree is one of the clearest examples of a legged robotics company moving from research visibility into real productisation and broader market distribution.