8 papers
SWIM: Vision-Language-Grounded Soft Whole-Body Interactive Manipulation
Tingcong Liu, Aye Phyu Phyu Aung, Junjie Xiong +4
Soft and continuum robots enable manipulation through distributed body deformation and contact, yet translating language and visual context into executable whole-body actuation rem…
Whole-Body Semantic-to-Actuation Grounding of Elephant-Inspired Soft-Trunk Motion via Lightweight Flow Matching
Tingcong Liu, Tongshun Chen, Siyi Ma +6
For close-contact human-robot interaction (HRI), trunk-like continuum manipulators provide a physical channel for diverse whole-body expression, but grounding open-vocabulary respo…
Physics Informed Generative Models for Magnetic Field Images
Aye Phyu Phyu Aung, Lucas Lum, Zhansen Shi +5
In semiconductor manufacturing, defect detection and localization are critical to ensuring product quality and yield. While X-ray imaging is a reliable non-destructive testing meth…
Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models
Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang +4
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) whe…
XMOL: Explainable Multi-property Optimization of Molecules
Aye Phyu Phyu Aung, Jay Chaudhary, Ji Wei Yoon +1
Molecular optimization is a key challenge in drug discovery and material science domain, involving the design of molecules with desired properties. Existing methods focus predomina…
Caption, Create, Continue: Continual Learning with Pre-trained Generative Vision-Language Models
Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar +1
Continual learning (CL) enables models to adapt to evolving data streams without catastrophic forgetting, a fundamental requirement for real-world AI systems. However, the current…
U-TELL: Unsupervised Task Expert Lifelong Learning
Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar +1
Continual learning (CL) models are designed to learn new tasks arriving sequentially without re-training the network. However, real-world ML applications have very limited label in…
Self-evolving Autoencoder Embedded Q-Network
J. Senthilnath, Bangjian Zhou, Zhen Wei Ng +7
In the realm of sequential decision-making tasks, the exploration capability of a reinforcement learning (RL) agent is paramount for achieving high rewards through interactions wit…