10 papers
A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta +3
While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation from example data points remains a…
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…
Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts
Minjae Kwon, Tyler Ingebrand, Ufuk Topcu +1
Unseen shifts in environment dynamics, driven by hidden parameters such as friction or gravity, create a challenge for maintaining safety. We address this challenge by proposing Ad…
MoS-VLA: A Vision-Language-Action Model with One-Shot Skill Adaptation
Ruihan Zhao, Tyler Ingebrand, Sandeep Chinchali +1
Vision-Language-Action (VLA) models trained on large robot datasets promise general-purpose, robust control across diverse domains and embodiments. However, existing approaches oft…
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…