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

TL;DR

This paper proposes a latent Trend ID-based framework for few-shot adaptation in non-stationary environments that provides a scalable and interpretable solution for robotics applications operating across diverse and evolving environments.

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.

Figures (6)

• Figure 1: Overview of the proposed Trend ID framework. Latent environment factors—such as robot configuration, target material properties, and ambient conditions—are embedded as low-dimensional Trend IDs in a continuous latent space. The deep model receives both observable sensor data and the estimated Trend ID to output a predicted distribution adapted to the current environment.
• Figure 2: Training phase
• Figure 3: Test phase
• Figure 5: Architecture of the proposed framework. The frozen feature extractor $F$ and trainable FC layer $G$ receive the input and Trend ID to output a predicted distribution. Trend IDs are derived from the state transition model (left), and trainable parameters (shaded) are optimized via backpropagation (dashed arrows).
• Figure 6: Structured trend space constructed during training. Each trajectory corresponds to a time sequence collected under a specific factory and date condition. Different colors denote different environmental conditions (factory/date/object type). The smooth trajectories indicate that the temporal constraint successfully enforces continuity in the latent space.