1 citations · 1 across the 7 of their papers we have counts for
9 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…
Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents
Hao-Lun Hsu, Nikki Lijing Kuang, Boyi Liu +2
Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be encoded in growing input conte…
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…
COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models
Ruiyang Wang, Hao-Lun Hsu, David Hunt +3
Autonomous exploration and object search in unknown indoor environments remain challenging for multi-robot systems (MRS). Traditional approaches often rely on greedy frontier assig…
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…
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Tianyuan Jin, Hao-Lun Hsu, William Chang +1
We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored into overlapping groups. Each group represents a hyperedge, forming a hypergraph over…