{
  "id": 10921,
  "url": "https://arxiv.org/abs/2607.13579v1",
  "title": "Agile perceptive multi-skill locomotion for quadrupedal robots in the wild",
  "summary": "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 utiliz",
  "authors": "Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song, Joon-Ha Kim, Seungwoo Hong, Hae-Won Park",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T08:22:52.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10921",
  "original_url": "https://arxiv.org/abs/2607.13579v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}