Add pipeline tag, links to paper/code/project, and improve documentation

README.md

@@ -1,43 +1,38 @@
 ---
+pipeline_tag: robotics
 tags:
 - robot manipulation
 - multi-modal perception
 - vision-language-action
 ---
 
-# UniLACT
+# UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models
 
-UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models.
+[**Paper**](https://huggingface.co/papers/2602.20231) | [**Project Page**](https://manishgovind.github.io/unilact-vla/) | [**Code**](https://github.com/ManishGovind/UniLACT)
+
+UniLACT is a transformer-based Vision-Language-Action (VLA) model that incorporates 3D geometric structure through depth-aware latent pretraining. By utilizing UniLARN, a unified latent action learning framework, the model learns a shared embedding space for RGB and depth, enabling downstream policies to inherit stronger spatial priors for precise and contact-rich robot manipulation.
 
 ## Abstract
-Latent action representations learned from unlabeled videos have recently emerged as a promising paradigm for
-pretraining vision-language-action (VLA) models without explicit robot action supervision. However, latent actions derived
-solely from RGB observations primarily encode appearancedriven dynamics and lack explicit 3D geometric structure,
-which is essential for precise and contact-rich manipulation. To address this limitation, we introduce UNILACT, a
-transformer-based VLA model that incorporates geometric
-structure through depth-aware latent pretraining, enabling
-downstream policies to inherit stronger spatial priors. To facilitate this process, we propose UNILARN, a unified latent action
-learning framework based on inverse and forward dynamics
-objectives that learns a shared embedding space for RGB and
-depth while explicitly modeling their cross-modal interactions.
-This formulation produces modality-specific and unified latent
-action representations that serve as pseudo-labels for the depthaware pretraining of UNILACT. Extensive experiments in both
-simulation and real-world settings demonstrate the effectiveness
-of depth-aware unified latent action representations. UNILACT
-consistently outperforms RGB-based latent action baselines
-under in-domain and out-of-domain pretraining regimes, as
-well as on both seen and unseen manipulation tasks.
+Latent action representations learned from unlabeled videos have recently emerged as a promising paradigm for pretraining vision-language-action (VLA) models without explicit robot action supervision. However, latent actions derived solely from RGB observations primarily encode appearance-driven dynamics and lack explicit 3D geometric structure. To address this limitation, we introduce UniLACT, which incorporates geometric structure through depth-aware latent pretraining. Our proposed UniLARN framework learns a shared embedding space for RGB and depth while explicitly modeling their cross-modal interactions. Extensive experiments demonstrate that UniLACT consistently outperforms RGB-based latent action baselines under both in-domain and out-of-domain pretraining regimes.
+
+## Setup
+
+```bash
+conda create -n unilact python=3.10 -y
+conda activate unilact
 
+git clone https://github.com/manishgovind/uniact-vla.git
+cd UniLACT
+pip install -r requirements.txt
+```
 
 ## Citation
 
 ```bibtex
-@misc{govind2026unilactdepthawarergblatent,
-  title={UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models},
-  author={Manish Kumar Govind and Dominick Reilly and Pu Wang and Srijan Das},
-  year={2026},
-  eprint={2602.20231},
-  archivePrefix={arXiv},
-  primaryClass={cs.RO},
-  url={https://arxiv.org/abs/2602.20231}
-}
+@article{govind2026unilactdepthawarergblatent,
+  title= {UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models},
+  author= {Manish Kumar Govind and Dominick Reilly and Pu Wang and Srijan Das},
+  journal={arXiv preprint arXiv:2602.20231},
+  year={2026}
+}
+```