13 citations · 37 across the 12 of their papers we have counts for
13 papers
REMM: Revisable Embodied Memory for Manipulation
Revanth Krishna Senthilkumaran, Ruwan Wickramarachchi, Alessandro Oltramari +1
Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence. Retaining observations alone does not…
GTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes
M. Yunus Seker, Shobhit Aggarwal, Ruwan Wickramarachchi +2
Robotic manipulation tasks are often decomposed into behaviors or skills. However, one often needs to predefine these behaviors for specific tasks or try to cover a wide range of t…
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam +5
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, comple…
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
Ruwan Wickramarachchi, Cory Henson, Amit Sheth
In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of Neurosymbolic AI has been shown to achi…
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Ishwar B Balappanawar, Ashmit Chamoli, Ruwan Wickramarachchi +3
Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distracti…
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing
Chathurangi Shyalika, Ruwan Wickramarachchi, Fadi El Kalach +2
Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important…
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti +4
The ability of Large Language Models (LLMs) to encode syntactic and semantic structures of language is well examined in NLP. Additionally, analogy identification, in the form of wo…
A Comprehensive Survey on Rare Event Prediction
Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth
Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, wh…
ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal G. Gajera +6
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods suc…
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems. KEP leverages relational knowledge from heterogeneous sources…
Neuro-symbolic Architectures for Context Understanding
Alessandro Oltramari, Jonathan Francis, Cory Henson +2
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisit…
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
Ruwan Wickramarachchi, Cory Henson, Amit Sheth
The autonomous driving (AD) industry is exploring the use of knowledge graphs (KGs) to manage the vast amount of heterogeneous data generated from vehicular sensors. The various ty…