BCAMP: A Behavior-Controllable Motion Control Method Based on Adversarial Motion Priors for Quadruped Robot

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.