Reactive Humanoid Multi-Contact Using Learned Stability Models
The research developed a reactive contact planner for humanoids that uses neural networks to predict stable Center of Pressure (CoP) placement after impacts, even on tilted or vertical surfaces. By modeling feasible CoP regions using optimization and learning, the system achieves significant stability improvements over simple methods. This allows the robot to use hand contacts quickly (under 10ms) for reliable recovery in real-world scenarios.