4 citations · 12 across the 14 of their papers we have counts for
7 papers · 1 filter
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene
Ruixiao Yang, Yilun Hao, Mingxin Yu +2
Vision-language models (VLMs) can reason about visual planning tasks, but querying them for every instance incurs repeated inference cost and latency. Generating reusable planning…
FederatedSkill: Federated Learning for Agentic Skill Evolution
Jingbo Yang, Guanyu Yao, Yang Zhang +3
Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement. However, isolated single-user task stre…
Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing
Zhiling Chen, David Gorsich, Matthew P. Castanier +3
Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor configuration rather than robot mo…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems
Jiabao Ji, Yongchao Chen, Yang Zhang +4
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints, including robot-robot collisions, robot-obstacle collisions, and…
How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings
Yujian Liu, Jiabao Ji, Li An +3
Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage perform…
VA-FastNavi-MARL: Real-Time Robot Control with Multimedia-Driven Meta-Reinforcement Learning
Yang Zhang, Shengxi Jing, Fengxiang Wang +2
Interpreting dynamic, heterogeneous multimedia commands with real-time responsiveness is critical for Human-Robot Interaction. We present VA-FastNavi-MARL, a framework that aligns…