Continual Learning in the Era of Foundation Models and Embodied Agents

This workshop focuses on continual learning as a shared challenge for foundation models and embodied agents in dynamic, open-ended, and interactive environments. We bring these areas together because they are increasingly intertwined in modern AI: foundation models are becoming central components of many embodied systems, while embodied settings place these models in changing conditions that demand continual adaptation. This creates overlapping challenges, including catastrophic forgetting, memory and knowledge consolidation, online adaptation, long-term skill acquisition, and safe model updates. The workshop is motivated by the growing recognition that static train-once paradigms are increasingly insufficient for real-world AI systems, which must adapt over time to new tasks, environments, and user needs. It aims to bring together researchers from continual learning, foundation models, robotics, and embodied AI to identify shared technical problems, discuss emerging methods and benchmarks, and encourage closer exchange across these communities.