2 citations · 2 across the 13 of their papers we have counts for
14 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…
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…
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}…
Zero-Shot Function Encoder-Based Differentiable Predictive Control
Hassan Iqbal, Xingjian Li, Tyler Ingebrand +4
We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neu…
Function Spaces Without Kernels: Learning Compact Hilbert Space Representations
Su Ann Low, Quentin Rommel, Kevin S. Miller +2
Function encoders are a recent technique that learn neural network basis functions to form compact, adaptive representations of Hilbert spaces of functions. We show that function e…
Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments
William Ward, Sarah Etter, Jesse Quattrociocchi +3
Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to i…
Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments
William Ward, Sarah Etter, Tyler Ingebrand +3
Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a cri…
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces
Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu
A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorith…
Basis-to-Basis Operator Learning Using Function Encoders
Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami +2
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We d…
Act Natural! Projecting Autonomous System Trajectories Into Naturalistic Behavior Sets
Hamzah I. Khan, Adam J. Thorpe, David Fridovich-Keil
Autonomous agents operating around human actors must consider how their behaviors might affect those humans, even when not directly interacting with them. To this end, it is often…
Zero-Shot Transfer of Neural ODEs
Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu
Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to u…
Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing
Yue Yu, Adam J. Thorpe, Jesse Milzman +2
Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is…
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
Junette Hsin, Shubhankar Agarwal, Adam Thorpe +2
In this paper, we address the challenge of deriving dynamical models from sparse and noisy data. High-quality data is crucial for symbolic regression algorithms; limited and noisy…