---
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}
}
```