Brain-Inspired Multi-Pathway Motion Decision-Making for Obstacle Avoidance of Humanoid Arms

Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion decision-making method is proposed to modulate rational planning and habitual actions of humanoid arms. Firstly, a novel framework integrating both a slow and a fast pathway is designed for motion decision-making tasks. Imitating the rational planning function of the prefrontal cortex, the slow pathway employs an improved planning approach based on Real-Time Rapidly exploring Random Tree Star (RT-RRT${}^{*}$) to execute deliberate decisions, along with an improvement in planning via the Smart technique and the high-efficiency neighbor searching method. Meanwhile, mimicking the habitual responses governed by the striatum, the fast pathway utilizes an action model trained by Soft Actor-Critic to make quick and habitual motions. The model in the fast pathway is also used to guide the sampling strategy in the slow pathway. Moreover, to facilitate the integration and smooth transition between the two pathways, an emotional neural network is designed as the modulation module with inspiration from the structure and function of the amygdala. Based on body and obstacle information, the network generates emotional signals to modulate the involvement degree of the two pathways before each decision-making process. Experimental results demonstrate that the proposed multi-pathway framework achieves a higher obstacle-avoidance success rate than existing methods while generating motion characteristics that are consistent with certain aspects of human obstacle-avoidance behavior.