Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors. We present APT-RL (Action Pretrained Transformer-based Reinforcement Learning), a unified framework that enables multi-skill locomotion to achieve high-speed traversal in complex environments through autonomous skill transitions utilizing only onboard perception and computation. Our approach generates large-scale, feature-rich 2D motion datasets through trajectory optimization with simplified dynamics. These datasets enable trai...
| Authors | Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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