5 papers
FORM: Robot Manipulation through Direct Material Law Identification
Stepan Tretiakov, Ruihan Zhao, Cheng-Hsi Hsiao +6
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online…
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…
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…
SetONet: A Set-Based Operator Network for Solving PDEs with Variable-Input Sampling
Stepan Tretiakov, Xingjian Li, Krishna Kumar
Most neural-operator surrogates for PDEs inherit from DeepONet-style formulations the requirement that the input function be sampled at a fixed, ordered set of sensors. This assump…