Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately. While reinforcement learning with velocity-commanded policies has achieved remarkable robustness in humanoid locomotion, this approach lacks explicit control of the foothold placement, leading to unsafe behavior, such as stepping onto human feet, or imprecise navigation, hindering the following manipulation task. Conversely, explicit foothold-tracking policies offer a promising alternative by directly being commanded with target foot poses. However, existing approaches are often limited by unrealistic state as...
| Authors | Alessandro Montenegro, Shihao Li, Puze Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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