###### Abstract  
In unpredictable scenarios, quadruped robots with behavior-controllable capabilities can often improve their adaptability through interaction with users. In this paper, we propose a behavior-controllable motion control method, integrating user commands with adversarial motion priors, enabling the quadruped robot to achieve behavior-controllable capabilities. Firstly, a motion trajectory library is constructed to provide motion prior knowledge. To obtain stable trajectory data for various motions, optimal control methods are used to generate dynamic trajectories with whole-body dynamic constraints. These trajectory data are then standardized and assigned different weights, resulting in the construction of a motion trajectory library for the quadruped robot. Secondly, an adversarial motion prior network structure combined with user commands is proposed. Reward functions tailored to different motion behaviors are designed to achieve behavior control. This network structure acts as a single-motion prior discriminator, which, compared to a multi-motion prior discriminator, avoids complex architectures. Furthermore, the incorporation of user commands effectively addresses the issue where the single-motion prior discriminator struggles to clearly select actions as the dataset expands. Finally, simulations and comparative experiments are conducted to evaluate the effectiveness of the proposed method. behavior-controllable; motion trajectory library; adversarial motion priors; user commands +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000  
With advancements in computing technologies and intelligent algorithms, deep reinforcement learning (DRL) has emerged as a more adaptive approach for controlling quadruped robots [8; 9]. A DRL-based robot controller has enabled a quadruped robot to learn walking in a real-world environment in just 20 min [10]. A behavior-diverse walking controller, named \"Walk These Ways\", has achieved the learning of multiple gaits through a single policy, enabling task diversification by adjusting gaits [11]. Robots such as ANYmal and ANYmal-C are capable of walking stably on complex natural terrains [12]. This is made possible by a DRL controller that does not rely on visual information but instead utilizes a teacher-student architecture and a terrain adaptation curriculum during training. Additionally, a behavior decision tree is introduced, which enables the control of various tasks through user commands [13]. By allowing users to adjust the robot's behavior according to specific needs, the robot can select different gaits or behavior patterns as required, thereby enhancing both adaptability and behavior controllability. However, these methods often depend on the design of complex reward functions to guide the learning process toward the desired control strategies, significantly increasing the difficulty of training. Data-driven methods aim to reduce the complexity of designing reward functions and improve learning efficiency for various movement behaviors and environments. Motion primitive-based hierarchical reinforcement learning decomposes complex tasks into reusable action primitives, enabling efficient learning and improved generalization to unseen scenarios by leveraging pre-optimized or learned motion modules [14; 15; 16; 17]. An imitation learning method utilizes action snippets as reference data, allowing the agent to mimic these snippets to acquire high-difficulty skills and predict subsequent actions based on images [18]. The Adversarial Motion Priors (AMP) method uses motion prior knowledge to construct an adversarial discriminator [19; 20]. The discriminator not only mimics the behaviors of motions in the dataset but also learns the prior distribution of these motions, capturing the naturalness, physical feasibility, and relationships between different actions.