CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task identifiers for deployment. To address these limitations, we propose CLARE, a general, parameter-efficient framework for exemplar-free continual learning with VLAs. CLARE introduces lightweight modular adapters into selected feedforward layers and autonomously expands the model only where necessary when learning a new task, guided by layer-wise feature similarity. During deployment, an autoencoder-based routing mechanism dynamically activates the most relevant adapters without requiring task labels. Through extensive experiments on the LIBERO benchmark, we show that CLARE achieves high performance on new tasks without catastrophic forgetting of earlier tasks, significantly outperforming even exemplar-based methods. Code and data are available at https://tum-lsy.github.io/clare.
696f6c1ebfeda8d7e160c9ca
Ralf Römer
claimed_verified
2026-01-22T17:12:27.188Z
67d9e92d6b2f730b7bd8386d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/tUecOSl7VCT0XW3ItVzlG.png
Ralf Römer
ralfroemer
user
ralfroemer
696f6c1ebfeda8d7e160c9cb
Yi Zhang
696f6c1ebfeda8d7e160c9cc
Angela P. Schoellig
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task identifiers for deployment. To address these limitations, we propose CLARE, a general, parameter-efficient framework for exemplar-free continual learning with VLAs. CLARE introduces lightweight modular adapters into selected feedforward layers and autonomously expands the model only where necessary when learning a new task, guided by layer-wise feature similarity. During deployment, an autoencoder-based routing mechanism dynamically activates the most relevant adapters without requiring task labels. Through extensive experiments on the LIBERO benchmark, we show that CLARE achieves high performance on new tasks without catastrophic forgetting of earlier tasks, significantly outperforming even exemplar-based methods. Code and data are available at https://tum-lsy.github.io/clare.
CLARE is a parameter-efficient, exemplar-free continual learning framework for vision-language-action models that enables robots to adapt to new tasks while preserving previously learned knowledge through lightweight adapters and dynamic routing mechanisms.
vision-language-action models continual learning parameter-efficient fine-tuning modular adapters feature similarity autoencoder-based routing LIBERO benchmark catastrophic forgetting