activity
20242026
most citedHow Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings

3 citations · 3 across the 7 of their papers we have counts for

collaborators
Showing cs.ROShow all

6 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning

Yilun Hao, Yongchao Chen, Chuchu Fan +1

Vision Language Models (VLMs) show strong potential for visual planning but struggle with precise spatial and long-horizon reasoning, while Planning Domain Definition Language (PDD…

cs.RO2025

Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation

Yongchao Chen, Yilun Hao, Yang Zhang +1

Recent works have shown great potentials of Large Language Models (LLMs) in robot task and motion planning (TAMP). Current LLM approaches generate text- or code-based reasoning cha…

cs.RO2025

Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners

Jiabao Ji, Yongchao Chen, Yang Zhang +4

Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even sta…