7 papers
DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning
Zeyu Gao, Yao Mu, Jinye Qu +7
Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies. Current methods using Large Language Models (LLMs) suffer fr…
Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent
Bowei Xia, Mengkang Hu, Shijian Wang +5
Research on self-evolving language agents has accelerated, drawing increasing attention to their ability to create, adapt, and maintain tools from task requirements. However, exist…
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
Mengkang Hu, Bowei Xia, Yuran Wu +9
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of l…
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Junting Chen, Haotian Liang, Lingxiao Du +8
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM)…
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Mengkang Hu, Yuhang Zhou, Wendong Fan +13
Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Curre…
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
Mengkang Hu, Tianxing Chen, Yude Zou +7
Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensivel…