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OpenGo, a robotic dog powered by OpenClaw, achieves real-time skill switching based on scene and task instructions. It uses a customizable skill library with autonomous validation, a dispatcher for skill selection based on prompts, and a self-learning framework for fine-tuning with human feedback. Experiments on a Unitree Go2 dog demonstrate autonomous skill switching and control via natural language through Feishu integration.
Imagine controlling a robotic dog with simple voice commands, instantly switching between complex skills like obstacle avoidance and object manipulation.
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.
