Few-Shot Adaptation to Non-Stationary Environments via Latent Trend Embedding for Robotics

ArXi:2603.10373v1 Announce Type: cross Robotic systems operating in real-world environments often suffer from concept shift, where the input-output relationship changes due to latent environmental factors that are not directly observable. Conventional adaptation methods update model parameters, which may cause catastrophic forgetting and incur high computational cost. This paper proposes a latent Trend ID-based framework for few-shot adaptation in non-stationary environments.