Evaluates embodied cleaning agents in physics-accurate indoor simulations, probing their ability to perform sweeping and grasping tasks across diverse cluttered scenes. It measures task completion, spatial coverage efficiency, motion quality, and collision safety under strict time limits. Use when the user wants to benchmark on CleanUpBench, or asks about evaluating this task. Reports TCR.
Scanned 9/11/2026
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---
name: cleanupbench-eval
description: Evaluates embodied cleaning agents in physics-accurate indoor simulations, probing their ability to perform sweeping and grasping tasks across diverse cluttered scenes. It measures task completion, spatial coverage efficiency, motion quality, and collision safety under strict time limits. Use when the user wants to benchmark on CleanUpBench, or asks about evaluating this task. Reports TCR.
metadata:
skill_kind: dataset_eval
source_arxiv: 2508.05543
bibtex_key: li2025cleanupbench
confidence: high
---
# cleanupbench-eval
> CleanUpBench: Embodied Sweeping and Grasping Benchmark — Li et al. (2025) (arXiv:2508.05543, 2025)
## What this evaluates
Evaluates embodied cleaning agents in physics-accurate indoor simulations, probing their ability to perform sweeping and grasping tasks across diverse cluttered scenes. It measures task completion, spatial coverage efficiency, motion quality, and collision safety under strict time limits.
## Datasets
- **CleanUpBench** — total 20; splits: test (20)
## Metrics
- `TCR` **(primary)** — range: [0, 1]
- Overall Task Completion Rate, calculated as the fraction of successfully completed cleaning tasks (sweeping and grasping) out of total targets.
- `TCR_S` — range: [0, 1]
- Sweep Task Completion Rate, measuring the fraction of sweeping targets successfully cleared.
- `TCR_G` — range: [0, 1]
- Grasp Task Completion Rate, measuring the fraction of grasping targets successfully picked up.
- `ME` — range: other
- Motion Efficiency, defined as total travel distance in meters divided by the number of targets.
- `SR` — range: [0, 1]
- Sweep Redundancy, quantifying overlapping or repeated sweeping actions relative to optimal coverage.
- `CR` — range: [0, 1]
- Coverage Rate, measuring the proportion of the floor area successfully swept.
- `FT` — range: other
- Task Completion Time, the total elapsed seconds until task finish or timeout.
- `CT` — range: other
- Computation Time, the processing time in seconds required for decision-making per step.
- `Vel_avg` — range: other
- Average Velocity, the mean speed in meters per second during operation.
- `Col` — range: other
- Total Collision Count, the number of physical collisions with obstacles or objects.
## Input / output format
**Input**: A physics-accurate simulation of an indoor cluttered environment with a mobile robot/manipulator, sensor modalities, and task goals (sweeping, grasping, or dual-mode) within a 300-second time limit.
**Output**: Continuous or discrete action commands for robot locomotion and arm manipulation, executed step-by-step until task completion or the 300-second timeout.
## Scoring recipe
```python
def score(trajectory, gold_tasks, time_limit=300):
total_targets = len(gold_tasks)
swept = count_completed_sweeps(trajectory)
grasped = count_completed_grasps(trajectory)
tcr = (swept + grasped) / total_targets
tcr_s = swept / count_sweep_targets(gold_tasks)
tcr_g = grasped / count_grasp_targets(gold_tasks)
me = trajectory.total_distance / total_targets
sr = 1.0 - (trajectory.swept_area / total_area)
cr = trajectory.swept_area / total_area
ft = min(trajectory.end_time, time_limit)
ct = trajectory.computation_time
vel_avg = trajectory.total_distance / ft if ft > 0 else 0
col = trajectory.collision_count
return {'TCR': tcr, 'TCR_S': tcr_s, 'TCR_G': tcr_g, 'ME': me, 'SR': sr, 'CR': cr, 'FT': ft, 'CT': ct, 'Vel_avg': vel_avg, 'Col': col}
```
## Common pitfalls
- Evaluating sweep-only or grasp-only baselines on both tasks without using the decomposed TCR_S/TCR_G metrics, which unfairly penalizes them with zero scores.
- Ignoring the strict 300-second time limit, which truncates trajectories and artificially deflates completion rates and inflates time-based metrics.
- Directly comparing single-robot and multi-robot methods on raw TCR without accounting for coordination overhead, computational time (CT), and collision penalties (Col).
## Evidence (verbatim from paper)
> TCR: Overall Task Completion Rate. TCR $_S$ : Sweep Task Completion Rate. TCR $_G$ : Grasp Task Completion Rate. ME: Motion Efficiency (m/target). SR: Sweep Redundancy. CR: Coverage Rate. FT: Task Completion Time (s). CT: Computation Time (s). Vel $_{\text{avg}}$ : Average Velocity (m/s). Col: Total Collision Count.
## Citation
```bibtex
@misc{li2025cleanupbench,
title={CleanUpBench: Embodied Sweeping and Grasping Benchmark},
author={Li et al. (2025)},
year={2025},
note={arXiv:2508.05543}
}
```
- arXiv: 2508.05543
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