Reinforcement learning-driven deep learning approaches for optimized robot trajectory planning

Trajectory planning and control of bipedal walking robots require precise joint torque computation to ensure stability and efficiency. Given the nonlinear dynamics and complex interactions of bipedal systems, achieving stable walking remains a major challenge. Deep reinforcement learning (DRL) offers a promising solution by directly mapping observed states to optimal actions that maximize cumulative rewards. In this work, we integrate deep learning-based trajectory planning with a DRL-driven control system to generate optimal joint torque sequences. Our approach aims to achieve stable walking with maximum forward speed, minimal power consumption, and enhanced stability to prevent falls. After training, the bipedal robot demonstrates stable and resilient locomotion, maintaining balance throughout the gait cycle. Additionally, it exhibits robust performance under uncertainties, handling mass variations of up to 20% and length variations of up to 5%. The robot effectively rejects disturbances at different angular velocities across various gait phases, enhancing adaptability. This approach improves the robustness and efficiency of bipedal robots, making them more suitable for real-world applications requiring reliable and adaptive locomotion.