Organizations: College of Information Science and Engineering, Ritsumeikan University, Osaka, Japan · College of Information Science and Engineering, Ritsumeikan University, Osaka, Japan. · Production and Technology Department, NIPPN CORPORATION, Tokyo, Japan · Production & Technology Department, NIPPN CORPORATION, Tokyo, Japan. · University of Osaka, Osaka, Japan · University of Osaka, Osaka, Japan. · National Institute of Technology, Ichinoseki College, Iwate, Japan · National Institute of Technology, Ichinoseki College, Iwate, Japan.
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 & tables
Fig. 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.
Fig. 2 : Architecture of the proposed Trend-ID based adaptation framework. (a) During training, both the fully connected layer G and the Trend IDs {zi} are jointly optimized while the feature extractor F is held fixed. (b) During testing, both F and G are frozen, and only the Trend ID ztest is optimized from a small number of observed samples via backpropagation.
Fig. 3 : 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).
Fig. 4 : 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.
Fig. 5 : Visualization of the estimated Trend IDs in the test environment after few-shot adaptation. Each point represents a Trend ID in the 2D latent space ( px , py ). The estimated Trend IDs are placed within the region spanned by the training latent space, suggesting that the model integrates the unseen environment into the existing latent structure using a small number of samples.
School of Computer Science and Engineering, Tongji University, Shanghai, China · School of Vehicle and Mobility, Tsinghua University, Beijing, China · Simple AI, Beijing, China +1
Department of Computer Science and Engineering, POSTECH, Pohang, South Korea · Graduate School of Artificial Intelligence, POSTECH, Pohang, South Korea