Reproduce “Y-BotFrame: An Extensible Embodied Agent Framework for Quadruped Robot Assistants” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Luyao Zhang, Ke Li, Yuan Ding, Xulong Zhao, Guo Yu, Chengwei Yan, Fuyu Dong, Jiawei Hu and 4 more
Researchers from Xidian University developed Y-BotFrame, an extensible embodied agent framework for quadruped robots that integrates multimodal perception and an LLM-based cognitive core. This framework translates natural language instructions into robot actions, supporting intuitive human-robot interaction and a modular, distributed system design for deploying general-purpose intelligent ground assistants.
Key insights
- The hierarchical decoupling of the cognitive LLM from encapsulated execution modules enhances the system's interpretability, extensibility, and stability, overcoming limitations of monolithic end-to-end approaches.
- Standardizing robot capabilities into 'plug-and-play' modules with well-defined interfaces facilitates modular upgrades and iterative development, allowing for easy integration of new functionalities.
- A distributed system infrastructure, offloading computationally intensive tasks to a remote server, reduces onboard computational load on the robot, improving deployment flexibility and operational efficiency.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.