48 citations · 155 across the 23 of their papers we have counts for
24 papers
Selective Backpropagation for Efficient Few-Shot Class-Incremental Learning
Eeham Khan, Abdulmoumen Al-Atrash, Ali Ayub
Few-Shot Class-Incremental Learning (FSCIL) requires models to continuously learn new classes from limited samples while retaining prior knowledge, under strict constraints on comp…
Abstention-Aware Personalized Object Rearrangement via Uncertainty-Guided LLM Assistance
Sam Collin, Ali Ayub
Robotic assistance in household environments requires not only predicting where objects should be placed, but also reasoning about when objects should not be placed at all. Existin…
Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
Alvaro Gonzalez, M. H. Hasan Shovo, Ali Ayub
Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy conditions. Existing approaches often…
ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations
Florent Chiaroni, Ali Ayub, Ola Ahmad
In robotics applications, few-shot segmentation is crucial because it allows robots to perform complex tasks with minimal training data, facilitating their adaptation to diverse, r…
Interactive Continual Learning Architecture for Long-Term Personalization of Home Service Robots
Ali Ayub, Chrystopher Nehaniv, Kerstin Dautenhahn
For robots to perform assistive tasks in unstructured home environments, they must learn and reason on the semantic knowledge of the environments. Despite a resurgence in the devel…
CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics
Ali Ayub, Alan R. Wagner
For most real-world applications, robots need to adapt and learn continually with limited data in their environments. In this paper, we consider the problem of Few-Shot class Incre…
Adapting to Human Preferences to Lead or Follow in Human-Robot Collaboration: A System Evaluation
Ali Noormohammadi-Asl, Ali Ayub, Stephen L. Smith +1
With the introduction of collaborative robots, humans and robots can now work together in close proximity and share the same workspace. However, this collaboration presents various…
How Do Human Users Teach a Continual Learning Robot in Repeated Interactions?
Ali Ayub, Jainish Mehta, Zachary De Francesco +3
Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow rob…
A Personalized Household Assistive Robot that Learns and Creates New Breakfast Options through Human-Robot Interaction
Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn
For robots to assist users with household tasks, they must first learn about the tasks from the users. Further, performing the same task every day, in the same way, can become bori…
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions
Ali Ayub, Zachary De Francesco, Patrick Holthaus +2
For long-term deployment in dynamic real-world environments, assistive robots must continue to learn and adapt to their environments. Researchers have developed various computation…
Few-Shot Continual Active Learning by a Robot
Ali Ayub, Carter Fendley
In this paper, we consider a challenging but realistic continual learning (CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is provided with unlabeled data…
Don't Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot
Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn
Assistive robots operating in household environments would require items to be available in the house to perform assistive tasks. However, when these items run out, the assistive r…
If you Cheat, I Cheat: Cheating on a Collaborative Task with a Social Robot
Ali Ayub, Huiqing Hu, Guangwei Zhou +4
Robots may soon play a role in higher education by augmenting learning environments and managing interactions between instructors and learners. Little, however, is known about how…
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning
Ali Ayub, Alan R. Wagner
Deep learning has achieved remarkable success in object recognition tasks through the availability of large scale datasets like ImageNet. However, deep learning systems suffer from…
Learning Novel Objects Continually Through Curiosity
Ali Ayub, Alan R. Wagner
Children learn continually by asking questions about the concepts they are most curious about. With robots becoming an integral part of our society, they must also learn unknown co…
Continual Learning of Visual Concepts for Robots through Limited Supervision
Ali Ayub, Alan R. Wagner
For many real-world robotics applications, robots need to continually adapt and learn new concepts. Further, robots need to learn through limited data because of scarcity of labele…
EEC: Learning to Encode and Regenerate Images for Continual Learning
Ali Ayub, Alan R. Wagner
The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cogniti…
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper
Ali Ayub, Alan R. Wagner
Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the soc…
Tell me what this is: Few-Shot Incremental Object Learning by a Robot
Ali Ayub, Alan R. Wagner
For many applications, robots will need to be incrementally trained to recognize the specific objects needed for an application. This paper presents a practical system for incremen…
Storing Encoded Episodes as Concepts for Continual Learning
Ali Ayub, Alan R. Wagner
The two main challenges faced by continual learning approaches are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose…
Cognitively-Inspired Model for Incremental Learning Using a Few Examples
Ali Ayub, Alan Wagner
Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catast…
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction
Ali Ayub, Alan R. Wagner
This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demon…
Centroid Based Concept Learning for RGB-D Indoor Scene Classification
Ali Ayub, Alan R. Wagner
This paper contributes a novel cognitively-inspired method for RGB-D indoor scene classification. High intra-class variance and low inter-class variance make indoor scene classific…
Using Markov Decision Process to Model Deception for Robotic and Interactive Game Applications
Ali Ayub, Aldo Morales, Amit Banerjee
This paper investigates deception in the context of motion using a simulated mobile robot. We analyze some previously designed deceptive strategies on a mobile robot simulator. We…