README.md

metadata

annotations_creators:
  - machine-generated
language:
  - en
language_creators:
  - machine-generated
license: mit
task_categories:
  - robotics
  - reinforcement-learning
  - video-to-video
task_ids:
  - grasping
  - task-planning
tags:
  - world-model
  - simulator
  - friction
  - contact-dynamics
  - physics-simulation
  - dynamics-prediction
pretty_name: DreamerBench
size_categories:
  - n<1K

Dataset Card for DreamerBench

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information

Dataset Description

• Homepage: [TODO: Link to project website or repository]
• Repository: https://github.com/uwsbel/ChronoDreamer
• Paper: [TODO: Link to arXiv paper if available]
• Point of Contact: Json Zhou, zzhou292@wisc.edu

Dataset Summary

DreamerBench is a large-scale dataset designed for training and evaluating World Models in robotics applications. Unlike standard visual-only datasets, DreamerBench explicitly focuses on physical interaction dynamics, specifically friction and contact data.

The dataset is generated using Project Chrono (https://projectchrono.org/), simulating diverse robotic interaction scenarios where precise modeling of physical forces is critical. It includes pre-computed encodings to facilitate efficient training of latent dynamics models.

Key features:

• Physical Fidelity: detailed ground-truth annotations for coefficient of friction, contact forces, and slip.
• Multi-Modal: Contains visual observations (RGB/Depth), proprioceptive states, and explicit physics parameters.
• World Model Ready: Structured to support next-step prediction and imaginary rollout training (Dreamer-style architectures).

Supported Tasks and Leaderboards

• World Modeling / Dynamics Learning: Training models to predict future states ($s_{t+1}$) given current state ($s_t$) and action ($a_t$).
• Offline Reinforcement Learning: Learning policies from the provided simulator trajectories without active environmental interaction.
• Sim-to-Real Adaptation: Using the varied friction/contact parameters to train robust policies that generalize to real-world physics.

Dataset Structure

Data Instances

Each instance in the dataset represents a trajectory or episode of a robot interacting with the environment.

Example structure (JSON/Parquet format):

{
  "episode_id": "traj_001",
  "steps": 1000,
  "observations": {
    "rgb": [Array of (1000, 64, 64, 3) images],
    "depth": [Array of (1000, 64, 64, 1) images],
    "proprioception": [Array of joint angles/velocities]
  },
  "actions": [Array of control inputs],
  "rewards": [Array of float scalars],
  "physics_data": {
    "contact_forces": [Array of 3D force vectors],
    "friction_coefficient": 0.8,
    "contact_detected": [Binary array]
  },
  "encoding": [Pre-computed latent vectors, e.g., VAE or RSSM states]
}

Example scenarios:

Visual Data Samples

Examples of 3 scenarios across 4 different camera angles (256x256).

Scenario	Ego	Side 1	Side 2	Contact Splat
flashlight-box
flashlight-coca
waterbottle-coca