Kavli Affiliate: Wei Gao
| First 5 Authors: TianChen Huang, TianChen Huang, , ,
| Summary:
Traversing narrow beams is challenging for humanoids due to sparse,
safety-critical contacts and the fragility of purely learned policies. We
propose a physically grounded, two-stage framework that couples an XCoM/LIPM
footstep template with a lightweight residual planner and a simple low-level
tracker. Stage-1 is trained on flat ground: the tracker learns to robustly
follow footstep targets by adding small random perturbations to heuristic
footsteps, without any hand-crafted centerline locking, so it acquires stable
contact scheduling and strong target-tracking robustness. Stage-2 is trained in
simulation on a beam: a high-level planner predicts a body-frame residual
(Delta x, Delta y, Delta psi) for the swing foot only, refining the template
step to prioritize safe, precise placement under narrow support while
preserving interpretability. To ease deployment, sensing is kept minimal and
consistent between simulation and hardware: the planner consumes compact,
forward-facing elevation cues together with onboard IMU and joint signals. On a
Unitree G1, our system reliably traverses a 0.2 m-wide, 3 m-long beam. Across
simulation and real-world studies, residual refinement consistently outperforms
template-only and monolithic baselines in success rate, centerline adherence,
and safety margins, while the structured footstep interface enables transparent
analysis and low-friction sim-to-real transfer.
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