robotics
The Humanoid Looked Beyond the Next Foothold
A recurrent predictive feature guides a locomotion policy across gaps, stepping stones and narrow stairs from one depth stream.
Summary
A recurrent predictive feature guides a locomotion policy across gaps, stepping stones and narrow stairs from one depth stream.
WM-LOCO jointly trains a recurrent world model with a PPO policy so near-future observations and rewards can shape foot placement without explicit foothold labels. In simulation it succeeded on gaps and stepping stones where the matched baseline failed, while matching stair success and improving stride efficiency and pelvis acceleration. The same policy ran on a Unitree G1 and averaged 93.3% success across the three terrain classes in the authors' physical tests.
Why it matters
A recurrent predictive feature guides a locomotion policy across gaps, stepping stones and narrow stairs from one depth stream.
Limits and context
No additional limitation was separately recorded.
Key claims
A recurrent predictive feature guides a locomotion policy across gaps, stepping stones and narrow stairs from one depth stream.
Evidence: source-2026-09-03-013
Sources
- arXiv preprint 2609.02542arXiv · primary research
Corrections
No corrections have been recorded for this story.