A Deep Reinforcement Learning Algorithm for Objects Balance Control with Hexapod Robot

Legged robots have been a prominent focus of research for an extensive period, owing to their enhanced stability and maneuverability in challenging terrains compared to wheeled robots. In recent decades, the application of reinforcement learning (RL) to train legged robots has yielded excellent results. This approach has effectively addressed numerous challenges that traditional methods struggled to overcome, such as navigating through complex environments. The advancements in RL for legged robots have significantly improved the feasibility and success of demanding applications, including exploration and resource delivery in outdoor settings. This paper introduces a training architecture for a hexapod robot based on the implementation of proximal policy optimization (PPO) on a GPU. This architecture enables the hexapod to transport objects across uneven terrain surfaces while adhering to input control commands. Our investigation extends to assessing the performance limitations of the hexapod robot in both flat and uneven ground environments, with a particular focus on evaluating the impact of different activation functions.