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license: mit
pipeline_tag: robotics
---
# DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
Official implementation of the RSS 2026 paper:
**DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation**
[[Paper](https://huggingface.co/papers/2605.20856)] [[Code](https://github.com/ReNginx/DISC)]
> Hanxiang Ren, Pei Zhou, Xunzhe Zhou, and Yanchao Yang
> Robotics: Science and Systems (RSS), 2026
## 🔍 Overview
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.
## 📦 Checkpoints
The repository currently contains two checkpoints to reproduce the results reported in the paper:
- `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).
## 📊 Evaluation
To evaluate a trained checkpoint on LIBERO-90, you can use the following command from the [official repository](https://github.com/ReNginx/DISC):
```bash
python train.py --config-name=libero_90_eval model=hylap +ckpt_path=/path/to/checkpoint.ckpt
```
## 📝 Citation
If you find this repository useful for your research, please consider citing our paper:
```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}
}
``` |