Deep Learning-Based Posture Control in Quadrupedal Robots

This paper achieves improvements in enhancing the cruising stability of quadrupedal robots, ensuring that speed does not undergo severe fluctuations amidst changes in the environment. We have achieved approximately a 30% increase in rewards, maintaining stability throughout simulation training. This study explores advancements in posture and speed control for quadrupedal robots navigating stair environments, employing the Evolutionary Trajectory Generator (ETG) algorithm for optimized trajectory planning. Despite significant improvements in sensor integration and control algorithms allowing for enhanced stability and smoother transitions, challenges persist in more complex terrains and under load-bearing conditions. This paper details the integration of deep learning techniques with traditional control systems to refine the robot's response to dynamic changes in terrain and assesses the impact of these technologies on the robot's operational efficiency. Future directions suggest expanding control over limb joints for better maneuverability during turns and addressing the limitations encountered in potholed terrains.