{"slug": "agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild", "title": "Agile perceptive multi-skill locomotion for quadrupedal robots in the wild", "summary": "Researchers developed APT-RL, a framework enabling quadrupedal robots to learn agile locomotion skills from trajectory-optimization data and apply them via reinforcement learning on complex terrain. The system generated 180,000 trajectories in 8 minutes, demonstrating rapid acquisition of reusable locomotion representations for real-world deployment.", "body_md": "APT-RL first learns reusable locomotion representations from trajectory-optimization data and then uses these representations as priors for reinforcement learning on complex terrain.\n\nTrajectory optimization based on single rigid body dynamics generated **180,000 trajectories\n(15.5 hours of motion) in 8 minutes**. The dataset contains both state trajectories and their\ncorresponding control inputs.", "url": "https://wpnews.pro/news/agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild", "canonical_source": "https://skillquadsr.github.io/", "published_at": "2026-07-16 12:41:08+00:00", "updated_at": "2026-07-16 12:55:16.756879+00:00", "lang": "en", "topics": ["robotics", "machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild", "markdown": "https://wpnews.pro/news/agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild.md", "text": "https://wpnews.pro/news/agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild.txt", "jsonld": "https://wpnews.pro/news/agile-perceptive-multi-skill-locomotion-for-quadrupedal-robots-in-the-wild.jsonld"}}