4 citations · 6 across the 11 of their papers we have counts for
11 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…
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…
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…
Separation Assurance in Urban Air Mobility Systems using Shared Scheduling Protocols
Surya Murthy, Tyler Ingebrand, Sophia Smith +3
Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and lo…
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…
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…
Zero-Shot Reinforcement Learning via Function Encoders
Tyler Ingebrand, Amy Zhang, Ufuk Topcu
Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The diff…
Fine-Tuning Language Models Using Formal Methods Feedback
Yunhao Yang, Neel P. Bhatt, Tyler Ingebrand +4
Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks.…