| license: cc-by-4.0 | |
| task_categories: | |
| - robotics | |
| tags: | |
| - simulation | |
| - panda | |
| - isaac-gym | |
| - robot | |
| - manipulation | |
| - push-t | |
| - imitation-learning | |
| - world-models | |
| # Panda Dataset | |
| [**Project Page**](https://taldatech.github.io/lpwm-web) | [**Paper**](https://huggingface.co/papers/2603.04553) | [**GitHub**](https://github.com/taldatech/lpwm) | |
| This repository contains expert imitation trajectories (actions and frames) for several robotic manipulation tasks, as presented in the paper [Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling](https://huggingface.co/papers/2603.04553). | |
| ## Dataset Description | |
| The data consists of expert trajectories generated using a **Panda robot** in the **IsaacGym** simulator. It was used to evaluate Latent Particle World Models (LPWM) in tasks requiring stochastic dynamics modeling and goal-conditioned imitation learning. | |
| - **Tasks**: | |
| - **Cube**: Manipulation of 1, 2, or 3 cubes. | |
| - **Push-T**: Pushing T-shaped objects (1, 2, or 3 T's). | |
| - **Observations**: 128x128 resolution RGB frames, including 2 camera views per task. | |
| - **Actions**: Continuous robot actions corresponding to the trajectories. | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{daniel2026latent, | |
| title={Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling}, | |
| author={Tal Daniel and Carl Qi and Dan Haramati and Amir Zadeh and Chuan Li and Aviv Tamar and Deepak Pathak and David Held}, | |
| booktitle={The Fourteenth International Conference on Learning Representations}, | |
| year={2026}, | |
| url={https://openreview.net/forum?id=lTaPtGiUUc} | |
| } | |
| ``` |