Robust Reinforcement Learning for Autonomous Robotics in Unstructured Environments

Autonomous robots operating in unstructured and dynamic environments face significant challenges due to unpredictable conditions and complex interactions. This paper proposes a novel robust reinforcement learning (RL) approach that enhances the adaptability and resilience of robotic systems. By incorporating uncertainty-aware training and adaptive control policies, the proposed RL framework enables robots to perform reliably in real-world scenarios, from logistics and manufacturing to exploration and disaster response, even in the presence of unforeseen disturbances.