RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control. (arXiv:2012.03094v3 [cs.RO] UPDATED)

We present a unified model-based and data-driven approach for quadrupedal
planning and control to achieve dynamic locomotion over uneven terrain. We
utilize on-board proprioceptive and exteroceptive feedback to map sensory
information and desired base velocity commands into footstep plans using a
reinforcement learning (RL) policy. This RL policy is trained in simulation
over a wide range of procedurally generated terrains. When ran online, the
system tracks the generated footstep plans using a model-based motion
controller. We evaluate the robustness of …

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