3 papers
cs.LG2026
Model-Based Reinforcement Learning for Control under Time-Varying Dynamics
Klemens Iten, Bruce Lee, Chenhao Li +3
Learning-based control methods typically assume stationary system dynamics, an assumption often violated in real-world systems due to drift, wear, or changing operating conditions.…
cs.RO2026
Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion
Mohamad H. Danesh, Chenhao Li, Amin Abyaneh +5
World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently. Yet the learned dynamics model…
cs.RO2023
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
Jin Cheng, Marin Vlastelica, Pavel Kolev +2
Despite many successful applications of data-driven control in robotics, extracting meaningful diverse behaviors remains a challenge. Typically, task performance needs to be compro…