Datasets:

zzhou292
/

DreamerBench

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label cosmos_tokenized
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label cosmos_tokenized

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Dataset Card for DreamerBench

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]
}