Addressing deficiencies in motion diversity and long-horizon task handling in quadruped robot algorithms, this study developed the PAWS algorithm. PAWS combines human feedback reinforcement learning with vision-language models (VLMs) to create an end-to-end, one-shot motion strategy. By analyzing video frames of canine behaviors, PAWS extracts key motion features using VLMs and designs new motion strategies for quadruped robots. This innovative method allows robots to learn from internet videos and directly map features to joint actions, bypassing traditional natural language instructions.