6 citations · 20 across the 14 of their papers we have counts for
14 papers
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
Shivam Goel, Yichen Wei, Panagiotis Lymperopoulos +3
As AI agents leave the lab and venture into the real world as autonomous vehicles, delivery robots, and cooking robots, it is increasingly necessary to design and comprehensively e…
A principled approach to model validation in domain generalization
Boyang Lyu, Thuan Nguyen, Matthias Scheutz +2
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen dom…
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Tung Thai, Ming Shen, Mayank Garg +10
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task perf…
Trade-off between reconstruction loss and feature alignment for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other…
Joint covariate-alignment and concept-alignment: a framework for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly…
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
Shivam Goel, Yash Shukla, Vasanth Sarathy +2
We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent's…
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos +3
In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty. However, visual novelty detection rese…
Conditional entropy minimization principle for learning domain invariant representation features
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite promising theory…
Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model
Felix Gervits, Gordon Briggs, Antonio Roque +4
Dialogue agents that interact with humans in situated environments need to manage referential ambiguity across multiple modalities and ask for help as needed. However, it is not cl…
Barycentric-alignment and reconstruction loss minimization for domain generalization
Boyang Lyu, Thuan Nguyen, Prakash Ishwar +2
This paper advances the theory and practice of Domain Generalization (DG) in machine learning. We consider the typical DG setting where the hypothesis is composed of a representati…
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
Felix Gervits, Antonio Roque, Gordon Briggs +2
Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world. To better un…
Robot Development and Path Planning for Indoor Ultraviolet Light Disinfection
Jonathan Conroy, Christopher Thierauf, Parker Rule +5
Regular irradiation of indoor environments with ultraviolet C (UVC) light has become a regular task for many indoor settings as a result of COVID-19, but current robotic systems at…
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Vasanth Sarathy, Daniel Kasenberg, Shivam Goel +2
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains. However, they are typically handcrafte…
"Can you do this?" Self-Assessment Dialogues with Autonomous Robots Before, During, and After a Mission
Tyler Frasca, Evan Krause, Ravenna Thielstrom +1
Autonomous robots with sophisticated capabilities can make it difficult for human instructors to assess its capabilities and proficiencies. Therefore, it is important future robots…