qhjqhj00 alrm-manipulation-eval
ALRM: Agentic LLM for Robotic Manipulation — Gaboardi dos Santos et al. (2026) (arXiv:2601.19510, 2026)
What this evaluates
Evaluates an agentic LLM's ability to plan and execute multistep robotic manipulation tasks in simulation. It tests closed-loop reasoning via ReAct-style loops, comparing code-as-policy and tool-as-policy execution modes across linguistically diverse tasks.
Datasets
- ALRM Simulation Benchmark — total 56; splits: test (-1)
Metrics
task_completion(primary) — range: [0, 1]- Binary success metric indicating whether the LLM agent successfully fulfills the user's high-level manipulation request within the maximum step limit.
Input / output format
Input: High-level natural language task descriptions or subtask templates (e.g., 'Pick the lemon and place it in the trash').
Output: Iterative sequences of thoughts, single-step actions (tool calls or Python code), and environmental observations until task completion or maximum steps.
Scoring recipe
```python
success = False
for step in range(max_steps):
action = model.generate_action(observation)
observation = env.execute(action)
if env.check_task_completion(observation):
success = True
break
return success
## Common pitfalls
- Simulation-to-real gap: Evaluation is conducted in Gazebo/MoveIt simulation, not on physical hardware.
- Code-as-Policy brittleness: Single syntax or logic errors in generated Python code cause immediate subtask failure.
- Step limit dependency: Performance heavily depends on the arbitrary maximum step count threshold.
## Evidence (verbatim from paper)
> The framework is evaluated on a novel benchmark of 56 linguistically diverse, multistep manipulation tasks across simulation environments, demonstrating superior performance in high-level reasoning and adaptability, with Claude-4.1-Opus and Falcon-H1-7B emerging as top performers in closed-loop and code-based execution respectively.
## Citation
```bibtex
@misc{gaboardidosantos2026alrm,
title={ALRM: Agentic LLM for Robotic Manipulation},
author={Gaboardi dos Santos et al. (2026)},
year={2026},
note={arXiv:2601.19510}
}
- arXiv: 2601.19510