multi-skill control 1perception 1quadrupedal locomotion 1reinforcement learning 1terrain navigation 1transformer models 1
From the 1 of 3 linked papers with an AI index.
3 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.RO2025
Reinforcement Learning-based Robust Wall Climbing Locomotion Controller in Ferromagnetic Environment
Yong Um, Young-Ha Shin, Joon-Ha Kim +2
We present a reinforcement learning framework for quadrupedal wall-climbing locomotion that explicitly addresses uncertainty in magnetic foot adhesion. A physics-based adhesion mod…
cs.RO2024
Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories
Gijeong Kim, Dongyun Kang, Joon-Ha Kim +2
This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or f…