Benchmark

The RoboLab Benchmark evaluates generalist robot manipulation policies across three competency axes spanning three difficulty levels. It consists of 120 tasks defined in robolab/tasks/benchmark/.

At a Glance

Stat	Value
Tasks	120
Average subtasks per task	2.02
Average objects per task	9.0
Average difficulty score	2.90
Evaluation time	~40 GPU hours / 120 tasks (assuming ~200ms inference step)
Average speed	1.4 it/s (assuming ~200ms inference step)

Difficulty distribution: simple 64 (53.3%) · moderate 39 (32.5%) · complex 17 (14.2%)

Competency Axes

The benchmark evaluates policies along three competency axes:

• Visual — recognition of visual traits: color, semantics, and size
• Relational — understanding of inter-object temporal, numerical, and spatial relationships
• Procedural — action-oriented reasoning including affordances, reorientation, and stacking

Task Attributes

Attributes are organized into three higher-level categories (mapping defined in robolab/constants.py → BENCHMARK_TASK_CATEGORIES):

Category	Attributes	Tasks
Visual	color (26), semantics (60), size (6)	84
Relational	conjunction (8), counting (7), spatial (29)	42
Procedural	affordance (12), reorientation (6), sorting (12), stacking (6)	34

Tasks may carry multiple attributes across axes. An additional vague tag (7 tasks) indicates ambiguous language instructions. 3 tasks are untagged.

Comparison to DROID Dataset

The benchmark emphasizes multi-step and two-step tasks compared to the DROID training distribution. In addition, only 68.7% of benchmark objects appear in the DROID training vocabulary (word-level overlap: 68 of 99 benchmark object words appear in DROID's 2,760-word vocabulary).

Difficulty Scoring

A task's difficulty score combines manipulation volume with skill complexity:

difficulty_score = num_subtasks + max(skill_weight)

where num_subtasks is the number of subtask actions and max(skill_weight) is the highest weight among the task's non-difficulty attributes. The skill weights are:

Weight	Attributes
0	color, semantics, size, conjunction, vague
+1	spatial
+2	counting, sorting, stacking, affordance
+3	reorientation

The score is mapped to a label using fixed thresholds (defined in DIFFICULTY_THRESHOLDS):

Label	Score Range
simple	score ≤ 2
moderate	score 3–4
complex	score ≥ 5

Key Files

File	Description
robolab/constants.py	SKILL_WEIGHTS, DIFFICULTY_THRESHOLDS, BENCHMARK_TASK_CATEGORIES
robolab/core/task/subtask_utils.py	compute_difficulty_score(num_subtasks, attributes)
robolab/tasks/_utils/load_task_info.py	Metadata pipeline — populates num_subtasks, difficulty_score, difficulty_label
robolab/tasks/_utils/generate_task_metadata.py	Generates task_metadata.json, task_report.txt, task_table.csv
robolab/tasks/_utils/compute_task_statistics.py	Prints summary statistics to stdout
robolab/tasks/_metadata/	Pre-generated metadata: task_metadata.json, task_report.txt, task_table.csv

Generating Statistics

# Print difficulty distribution
python robolab/tasks/_utils/compute_task_statistics.py --difficulty

# Include per-task breakdown
python robolab/tasks/_utils/compute_task_statistics.py --difficulty -v

# Regenerate metadata files (task_metadata.json, task_report.txt, task_table.csv)
python robolab/tasks/_utils/generate_task_metadata.py