6 papers
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
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)…
Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation
Yue Yang, MingKang Chen, Qihua Liu +9
Recent advances in large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking. However, whether LLMs possess genuine fluid intel…
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…
DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
Zhixuan Liang, Yao Mu, Yixiao Wang +6
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks,…
EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents
Junting Chen, Checheng Yu, Xunzhe Zhou +7
Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based m…