activity
20232025
most citedFine-Tuning Language Models Using Formal Methods Feedback

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2025

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…

cs.RO2025

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…

cs.AI2024

Joint Verification and Refinement of Language Models for Safety-Constrained Planning

Yunhao Yang, Neel P. Bhatt, William Ward +3

Large language models possess impressive capabilities in generating programs (e.g., Python) from natural language descriptions to execute robotic tasks. However, these generated pr…

cs.AI2023★ 1 cited

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.…

cs.GT2023

Active Inverse Learning in Stackelberg Trajectory Games

William Ward, Yue Yu, Jacob Levy +3

Game-theoretic inverse learning is the problem of inferring a player's objectives from their actions. We formulate an inverse learning problem in a Stackelberg game between a leade…