1 citations · 1 across the 6 of their papers we have counts for
8 papers
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation
Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng +2
Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks in complex environments. How…
GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
Ruiyang Wang, Hao-Lun Hsu, Swarajh Mehta +3
Large language models (LLMs) provide a flexible interface for long-horizon robot planning, but generated plans often fail to respect embodiment constraints, recover from planning e…
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang +2
In offline-to-online reinforcement learning (O2O-RL), policies are first safely trained offline using previously collected datasets and then further fine-tuned for tasks via limite…
Semantic Area Graph Reasoning for Multi-Robot Language-Guided Search
Ruiyang Wang, Hao-Lun Hsu, Jiwoo Kim +1
Coordinating multi-robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond geometric exploration. Classic…
NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces
Jiwoo Kim, Swarajh Mehta, Hao-Lun Hsu +3
Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matrix dimensions. Generation is fur…
Off-Policy Selection for Initiating Human-Centric Experimental Design
Ge Gao, Xi Yang, Qitong Gao +3
In human-centric tasks such as healthcare and education, the heterogeneity among patients and students necessitates personalized treatments and instructional interventions. While r…
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
Hao-Lun Hsu, Weixin Wang, Miroslav Pajic +1
We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for rando…
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
Hao-Lun Hsu, Qitong Gao, Miroslav Pajic
Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to…