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Reliable bipedal walking over complex terrain is a challenging problem, usinga curriculum can help learning. Curriculum learning is the idea of startingwith an achievable version of a task and increasing the difficulty as a successcriteria is met. We propose a 3-stage curriculum to train Deep ReinforcementLearning policies for bipedal walking over various challenging terrains. In thefirst stage, the agent starts on an easy terrain and the terrain difficulty isgradually increased, while forces derived from a target policy are applied tothe robot joints and the base. In the second stage, the guiding forces aregradually reduced to zero. Finally, in the third stage, random perturbationswith increasing magnitude are applied to the robot base, so the robustness ofthe policies are improved. In simulation experiments, we show that our approachis effective in learning walking policies, separate from each other, for fiveterrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstratethat in the absence of human demonstrations, a simple hand designed walkingtrajectory is a sufficient prior to learn to traverse complex terrain types. Inablation studies, we show that taking out any one of the three stages of thecurriculum degrades the learning performance.

Guided Curriculum Learning for Walking Over Complex Terrain