###### Abstract
Methods based on AMP and teacher-student networks have been employed to train controllers capable of achieving task objectives while exhibiting robustness [21; 22]. Additionally, the Multiple Adversarial Motion Priors (Multi-AMP) method incorporates multiple adversarial discriminators to learn various styles and skills from the motion dataset [23]. A wheeled-legged quadruped robot employing this method has successfully implemented different motion pattern transitions in the real world. These data-driven methods reduce the complexity of designing reward functions. By learning multiple diverse styles and skills, the robot can flexibly select and switch behavior patterns based on task requirements, thereby enhancing the diversity and controllability of its behavior. However, the motion datasets used in these methods typically originate from motion capture, which may contain unstable or unsafe actions. Furthermore, the highly structured data generated by motion capture incurs significant costs for data acquisition. The AMP-based method reduces the complexity of designing numerous complex reward functions [24], but it faces a challenge: when the dataset is expanded or when multiple trajectory data overlap for similar tasks, the policy struggles to select actions with a distinct style, leading to a decrease in behavior controllability. The Multi-AMP method improves behavior controllability by addressing the challenge of switching between different motion styles through the use of multiple discriminators. However, a limitation of Multi-AMP is that its network structure becomes large and scales with the increase in motion reference data, resulting in higher computational demands and greater training complexity. This paper proposes a control method based on Behavior-Controllable Adversarial Motion Priors (BCAMP) that enables quadruped robots to learn diverse and stable motions while acquiring behavior-controllable capabilities. First, to address the instability and safety concerns associated with motion capture data, optimal control methods are employed to generate dynamic trajectories with strict dynamic constraints. Various trajectory sequences are standardized to construct a quadruped robot motion trajectory library. Second, the method constructs a single adversarial discriminator and designs a network structure for the AMP and reward function tailored to the motion trajectory library. User commands are introduced to adjust the reward function and switch motion behaviors, enabling the policy network to perform multi-task learning and to control the quadruped robot motion behaviors. Finally, the method proposed in this paper is applied to the Unitree A1 quadruped robot, with simulation and comparative experiments conducted for validation. The construction of this paper is as follows. In Section 2, we provide an in-depth explanation of the process for generating the motion trajectory library. In Section 3, we introduce the comprehensive BCAMP framework, detailing the integration of user commands into the reward function. In Section 4, we present the simulation verification along with comparative experiments, followed by a thorough analysis of the results. Finally, in Section 5, we summarize the conclusions of our work in this study.