Datasets:

SemVLA
/

EmbodimentSemantic

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label EmbodimentSemantic@225db821c5c31fd0a3a3148b61ca026aa114fe9f
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 EmbodimentSemantic@225db821c5c31fd0a3a3148b61ca026aa114fe9f

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EmbodimentSemantic

A Spatial Scene-Graph Dataset and Benchmark for Vision-Language Models on Embodied Manipulation Trajectories

Hassan Jaber¹ · Refinath S N² · Luca Cagliero¹ · Christopher E. Mower² · Haitham Bou-Ammar²³

¹Politecnico di Torino · ²Huawei Noah's Ark Lab · ³University College London

Overview

EmbodimentSemantic is a benchmark dataset for evaluating whether vision-language models (VLMs) can recover exact spatial scene graphs from robot manipulation observations — and whether injecting those scene graphs into existing VLA policies improves downstream control.

The dataset has two components:

LIBERO Simulator Benchmark — 500 demonstrations across 10 LIBERO-Spatial tasks (62,250 paired timesteps, 124,500 RGB frames). Ground-truth scene graphs are derived automatically from MuJoCo geometry, giving exact triplet-level supervision without manual annotation.
SO101 Real-Robot Dataset — 257 teleoperated episodes across 5 tabletop bowl-placement tasks, collected with the low-cost SO101 arm. Includes external-camera, wrist-camera, and depth streams in LeRobot format.

Files

File	Size	Description
`libero_spatial_v5.zip`	2.91 GB	LIBERO simulator benchmark: HDF5 demos with scene-graph annotations embedded under `obs/agentview_scene_graph` and `obs/robot0_eye_in_hand_scene_graph`
`SO1001_dataset.zip`	6.13 GB	SO101 real-robot dataset in LeRobot format

Dataset Statistics

LIBERO Simulator Benchmark

Attribute	Value
Tasks	10
Demonstrations	500 (50 per task)
Recorded frames per camera	62,250
Total recorded frames (both cameras)	124,500
Frames per demo	75–197 (mean 124.5)
Cameras	`agentview`, `eye_in_hand`
RGB resolution	128 × 128
Mean triplets / frame (agentview)	42.0
Mean triplets / frame (eye_in_hand)	16.73

SO101 Real-Robot Dataset

Attribute	Value
Tasks	5
Demonstrations	257 (47–53 per task)
Total recorded frames (both cameras)	240,598
VLM eval frames (both cameras)	8,252
Cameras	`agent_view`, `wrist`
Frame rate	30 FPS (1 frame/sec sampled)
Format	LeRobot

Spatial Ontology

Objects (LIBERO): akita_black_bowl_1, akita_black_bowl_2, cookies_1, glazed_rim_porcelain_ramekin_1, plate_1, wooden_cabinet_1, flat_stove_1

Objects (SO101): black_bowl, red_drawer, black_stove, cookie, white_plate

Relations (both):

Relation	Description
`is_left_of` / `is_right_of`	Lateral world-frame ordering
`is_in_front_of` / `is_behind`	Depth world-frame ordering
`is_on_top_of` / `is_below_of`	Vertical support / stacking
`is_inside` / `contains`	Containment

Downloads last month: 48

Total file size:

9.04 GB