Safeqil Eval

---
name: safeqil-eval
description: This evaluation probes an agent's ability to learn safe navigation and manipulation policies from human demonstrations in environments with unknown safety constraints. It specifically tests the trade-off between maximizing task reward and minimizing safety violations (cost) under out-of-distribution conditions. Use when the user wants to benchmark on Safety-Gymnasium (SafetyPointGoal1-v0, SafetyPointCircle2-v0, SafetyCarButton1-v0, SafetyCarPush2-v0), or asks about evaluating this task. Reports episodic reward.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2602.23816
  bibtex_key: [REDACTED]
  confidence: high
---

# safeqil-eval

> Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective — Papadopoulos et al. (2026) (arXiv:2602.23816, 2026)

## What this evaluates

This evaluation probes an agent's ability to learn safe navigation and manipulation policies from human demonstrations in environments with unknown safety constraints. It specifically tests the trade-off between maximizing task reward and minimizing safety violations (cost) under out-of-distribution conditions.

## Datasets

- **Safety-Gymnasium (SafetyPointGoal1-v0, SafetyPointCircle2-v0, SafetyCarButton1-v0, SafetyCarPush2-v0)** — total ?; splits: test (4)

## Metrics

- `episodic reward` **(primary)** — range: other
  - Sum of step-wise rewards collected over a single evaluation episode. Higher values indicate better task performance.
- `safety cost` — range: other
  - Sum of constraint violation penalties collected over a single evaluation episode. Lower values indicate safer behavior.

## Input / output format

**Input**: State observations from the Safety-Gymnasium environment (e.g., agent position, velocities, obstacle locations).

**Output**: Action vector selected by the policy for the current state.

## Scoring recipe

```python
all_rewards, all_costs = [], []
for seed in range(3):
    for _ in range(40):
        ep_r, ep_c = run_episode(policy, env, seed)
        all_rewards.append(ep_r)
        all_costs.append(ep_c)
mean_r, std_r = np.mean(all_rewards), np.std(all_rewards)
mean_c, std_c = np.mean(all_costs), np.std(all_costs)
return f"{mean_r:.2f} ± {std_r:.2f}", f"{mean_c:.2f} ± {std_c:.2f}"
```

## Common pitfalls

- Over-constraining the policy to strictly match human demonstrations can cause catastrophic reward collapse, failing the task entirely.
- Baseline hyperparameters (especially for ICRL/VICRL) require extensive tuning per task to avoid poor safety-reward trade-offs.
- Reporting only mean values without standard deviation across seeds hides high variance in safety-critical tasks.

## Evidence (verbatim from paper)

> For each task, every method is trained with the same set of 40 human-generated demonstration trajectories. After training, we run 40 evaluation episodes per method. We measure episodic reward (higher is better) and safety cost (lower is better) on the 4 Safety-Gymnasium tasks. We repeat this with 3 independent random seeds and report mean and standard deviation (in the form mean ± std) over seeds, following recommended reporting practices for reliability.

## Citation

```bibtex
@misc{papadopoulos2026safeqil,
  title={Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective},
  author={Papadopoulos et al. (2026)},
  year={2026},
  note={arXiv:2602.23816}
}
```

- arXiv: 2602.23816