Lifelong Robot

Lifelong robot learning aims to create robots capable of continuously acquiring new skills and retaining previously learned ones without catastrophic forgetting, a major challenge in traditional machine learning. Current research focuses on developing efficient memory management techniques, leveraging large language models for task planning and skill acquisition, and employing novel architectures like RWKV for improved sequence modeling and decision-making in dynamic environments. This field is crucial for advancing robotics, enabling robots to adapt to unpredictable situations and operate effectively in real-world settings, ultimately impacting areas like assistive robotics and human-robot collaboration.