4 papers · 1 filter
End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
Xingjian Li, Kelvin Kan, Deepanshu Verma +3
We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs…
Path Planning in Physically Viable World Models
Su Ann Low, Cheng-Hsi Hsiao, Xingjian Li +3
Robots deployed in unstructured outdoor environments often plan from scene reconstructions collected before deployment because operators cannot remap large or remote sites before e…
Physically Viable World Models: A Case for Query-Conditioned Embodied AI
Adam J. Thorpe, Stepan Tretiakov, Cheng-Hsi Hsiao +6
World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing action outcomes, rather than mer…
Neural Operators for Multi-Task Control and Adaptation
David Sewell, Xingjian Li, Stepan Tretiakov +2
Neural operator methods have emerged as powerful tools for learning mappings between infinite-dimensional function spaces, yet their potential in optimal control remains largely un…