Adaptation to complex tasks and multiple scenarios remains a significant challenge for a single robot agent. The ability to acquire organize, and switch between a wide range of skills in real time, particularly in dynamic environments, has become a fundamental requirement for embodied intelligence. We introduce OpenGo, an OpenClaw-powered embodied robotic dog capable of switching skills in real time according to the scene and task instructions. Specifically, the agent is equipped with (1) a customizable skill library with easy skill import and autonomous skill validation, (2) a dispatcher that selects and invokes different skills according to task prompts or language instructions, and (3) a self-learning framework that fine-tunes skills based on task completion and human feedback. We deploy the agent in Unitree's Go2 robotic dog and validate its capabilities in self-checking and switching of skills autonomously. In addition, by integrating Feishu-platform communication, we enable natural-language guidance and human feedback, allowing inexperienced users to control the robotic dog through simple instructions.
Cite
@article{arxiv.2604.01708,
title = {OpenGo: An OpenClaw-Based Robotic Dog with Real-Time Skill Switching},
author = {Hanbing Li and Xuewei Cao and Zhiwen Zeng and Yuhan Wu and Yanyong Zhang and Yan Xia},
journal= {arXiv preprint arXiv:2604.01708},
year = {2026}
}