| --- |
| license: mit |
| pipeline_tag: robotics |
| --- |
| |
| # DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation |
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| Official implementation of the RSS 2026 paper: |
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| **DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation** |
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| [[Paper](https://huggingface.co/papers/2605.20856)] [[Code](https://github.com/ReNginx/DISC)] |
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| > Hanxiang Ren, Pei Zhou, Xunzhe Zhou, and Yanchao Yang |
| > Robotics: Science and Systems (RSS), 2026 |
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| ## 🔍 Overview |
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| DISC is a policy-generation framework that decouples high-level language instructions from state-conditioned low-level control. Rather than conditioning a universal policy on language, DISC uses a hypernetwork to generate the entire parameter set of a task-specific visuomotor policy from the instruction alone. This repository provides training, evaluation, and test-time training code for reproducing the results reported in the paper. |
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| ## 📦 Checkpoints |
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| The repository currently contains two checkpoints to reproduce the results reported in the paper: |
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| - `DISC-libero90.ckpt`: Model trained on the LIBERO-90 dataset (Table 1). |
| - `DISC-libero_cotrain_spatial_object_goal.ckpt`: Model co-trained on LIBERO-spatial, LIBERO-object, and LIBERO-goal (Table 2). |
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| ## 📊 Evaluation |
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| To evaluate a trained checkpoint on LIBERO-90, you can use the following command from the [official repository](https://github.com/ReNginx/DISC): |
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| ```bash |
| python train.py --config-name=libero_90_eval model=hylap +ckpt_path=/path/to/checkpoint.ckpt |
| ``` |
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| ## 📝 Citation |
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| If you find this repository useful for your research, please consider citing our paper: |
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| ```bibtex |
| @inproceedings{ren2026disc, |
| title={{DISC}: Decoupling Instruction from State-Conditioned Control via Policy Generation}, |
| author={Hanxiang Ren and Pei Zhou and Xunzhe Zhou and Yanchao Yang}, |
| booktitle={Robotics: Science and Systems}, |
| year={2026}, |
| url={https://openreview.net/forum?id=i9ynkagJuj} |
| } |
| ``` |
