Continual Task

Continual task learning (CTL) focuses on developing systems capable of acquiring new skills sequentially without forgetting previously learned ones, mirroring human learning. Current research emphasizes mitigating "catastrophic forgetting" through architectures like Mixture-of-Experts models and algorithms incorporating techniques from reinforcement learning and real-time scheduling, particularly for robotic applications. This field is crucial for advancing artificial intelligence, enabling the development of more adaptable and robust systems for diverse applications ranging from robotics and manufacturing to personalized AI assistants. Addressing the challenges of CTL is key to building truly intelligent systems that can learn and adapt continuously throughout their operational lifetime.