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

TL;DR

This paper introduces a novel few-shot adaptation framework for robotics in non-stationary environments, utilizing latent trend embeddings to adapt without changing model weights, thus enabling scalable and interpretable adaptation.

Contribution

The paper proposes a latent Trend ID-based method that estimates environmental states via backpropagation, avoiding catastrophic forgetting and reducing computational costs.

Findings

Effective adaptation to unseen environments with few samples

Latent Trend IDs exhibit temporally consistent trajectories

Framework achieves adaptation without modifying model weights

Abstract

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. Instead of modifying model weights, a low-dimensional environmental state, referred to as the Trend ID, is estimated via backpropagation while the model parameters remain fixed. To prevent overfitting caused by per-sample latent variables, we introduce temporal regularization and a state transition model that enforces smooth evolution of the latent space. Experiments on a quantitative food grasping task demonstrate that the learned Trend IDs are distributed across distinct regions of the latent space with temporally consistent trajectories, and that few-shot adaptation to unseen environments is achieved without modifying model parameters. The proposed framework provides a scalable and interpretable solution for robotics applications operating across diverse and evolving environments.