multi-skill control 1perception 1quadrupedal locomotion 1reinforcement learning 1terrain navigation 1transformer models 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.RO2026
Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song +3
The paper presents APT-RL, a transformer‑based reinforcement learning framework that learns multiple locomotion skills from simulated data and enables a quadrupedal robot to traver…
cs.RO2026
Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer
Dongyun Kang, Min-Gyu Kim, Tae-Gyu Song +3
Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing…