Kavli Affiliate: Wei Gao
| First 5 Authors: TianChen Huang, TianChen Huang, , ,
| Summary:
Traversing narrow paths is challenging for humanoid robots due to the sparse
and safety-critical footholds required. Purely template-based or end-to-end
reinforcement learning-based methods suffer from such harsh terrains. This
paper proposes a two stage training framework for such narrow path traversing
tasks, coupling a template-based foothold planner with a low-level foothold
tracker from Stage-I training and a lightweight perception aided foothold
modifier from Stage-II training. With the curriculum setup from flat ground to
narrow paths across stages, the resulted controller in turn learns to robustly
track and safely modify foothold targets to ensure precise foot placement over
narrow paths. This framework preserves the interpretability from the
physics-based template and takes advantage of the generalization capability
from reinforcement learning, resulting in easy sim-to-real transfer. The
learned policies outperform purely template-based or reinforcement
learning-based baselines in terms of success rate, centerline adherence and
safety margins. Validation on a Unitree G1 humanoid robot yields successful
traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.
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