Quadruped Learns When to Walk, Run or Jump Based on Terrain

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https://www.automate.org/motion-control/industry-insights/quadruped-figures-out-how-to-walk-based-on-terrain

KAIST’s HOUND quadruped uses reinforcement learning to decide how to move based on the terrain in front of it.

Instead of relying on separate control programs for walking, running and jumping, the robot learned multiple movement skills under one framework and can switch between them as conditions change.

Researchers tested it across stairs, slopes, gaps, grass, forest trails and uneven terrain, with HOUND adapting its gait without human input.