3 citations · 5 across the 7 of their papers we have counts for
7 papers
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy
Erfaun Noorani, Zachary Serlin, Ben Price +1
The DARPA Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT) program aims to address rapid and robust transfer of autonomy technologies across dynamic…
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam +3
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender s…
Imperceptible Adversarial Examples in the Physical World
Weilin Xu, Sebastian Szyller, Cory Cornelius +5
Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing simil…
Criticality and Safety Margins for Reinforcement Learning
Alexander Grushin, Walt Woods, Alvaro Velasquez +1
State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during…
Combining AI Control Systems and Human Decision Support via Robustness and Criticality
Walt Woods, Alexander Grushin, Simon Khan +1
AI-enabled capabilities are reaching the requisite level of maturity to be deployed in the real world, yet do not always make correct or safe decisions. One way of addressing these…
SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
Abhinav Rajvanshi, Karan Sikka, Xiao Lin +3
Semantic reasoning and dynamic planning capabilities are crucial for an autonomous agent to perform complex navigation tasks in unknown environments. It requires a large amount of…