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--- |
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license: apache-2.0 |
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library_name: lerobot |
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pipeline_tag: robotics |
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tags: |
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- robotics |
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- lerobot |
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--- |
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# CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion |
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DiT-EncDec base checkpoint from "CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion", pretrained on LIBERO-90. |
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- **Paper:** [CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion](https://huggingface.co/papers/2601.09512) |
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- **Project Page:** [tum-lsy.github.io/clare/](https://tum-lsy.github.io/clare/) |
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- **Repository:** [utiasDSL/clare](https://github.com/utiasDSL/clare) |
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## Description |
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CLARE is a general, parameter-efficient framework for exemplar-free continual learning with Vision-Language-Action (VLA) models. It 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. |
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This specific repository contains the **DiT-EncDec** base checkpoint used for pretraining on the **LIBERO-90** benchmark. |
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## Usage |
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To use this checkpoint for training on the LIBERO-10 benchmark using the CLARE framework, you can use the following command from the [official repository](https://github.com/utiasDSL/clare): |
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```bash |
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python ./lerobot_lsy/src/lerobot/scripts/clare.py \ |
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--seed=1000 \ |
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--job_name=clare_libero_10_task_0 \ |
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--output_dir=./outputs/clare_libero_10_task_0 \ |
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--dataset.repo_id=continuallearning/libero_10_image_task_0 \ |
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--policy.path=continuallearning/dit_mt_libero_90_pretrain \ |
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--policy.push_to_hub=false \ |
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--batch_size=32 \ |
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--num_workers=16 \ |
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--steps=20000 \ |
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--env.type=libero \ |
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--env.task=Libero_10_Task_0 \ |
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--eval.batch_size=20 \ |
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--eval.n_episodes=100 \ |
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--eval.max_episodes_rendered=100 \ |
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--eval_freq=200000 \ |
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--save_freq=20000 \ |
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--log_freq=100 \ |
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--peft_cfg_path=./peft_lsy/config \ |
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--expand_threshold=10.00 \ |
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--detect_distribution_shift_steps=200 \ |
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--detect_distribution_shift_batch_size=32 \ |
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--detect_distribution_shift_num_workers=16 \ |
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--detect_distribution_shift_log_freq=10 \ |
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--train_discriminators_steps=2000 \ |
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--train_discriminators_batch_size=32 \ |
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--train_discriminators_num_workers=16 \ |
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--train_discriminators_log_freq=50 \ |
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--train_discriminators_eval_freq=2000 \ |
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--train_discriminators_save_freq=2000 \ |
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--wandb.enable=true |
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``` |
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## BibTeX |
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```bibtex |
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@article{romer2026clare, |
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title={CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion}, |
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author={Ralf R{\"o}mer and Yi Zhang and Angela P. Schoellig}, |
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journal={arXiv preprint arXiv:2601.09512}, |
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year={2026} |
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} |
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``` |
