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TRAIN: A reinforcement learning framework for terrain-aware interactive navigation for quadruped robots

TRAIN proposes a two-stage deep reinforcement learning framework that, through the TRAIN-Loco and TRAIN-Nav pipelines, enables quadruped robots to achieve terrain-aware navigation in unstructured terrain and human-populated environments. Benchmarks compared quadruped locomotion and robot navigation baselines, and hardware experiments verified its sim-to-real transfer potential.

Part of the event: TRAIN: A Reinforcement Learning-Based Terrain-Aware Navigation Framework for Quadruped Robots

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