# OpenClaw Paper Outline Example

This outline is intentionally conservative. It assumes the evidence pack has been built, but strong performance claims remain gated on verified internal results.

## Title candidates

- OpenClaw: An Evidence-Grounded Workflow for Manipulation System Evaluation
- OpenClaw: Structuring Manipulation Research Around Verifiable Evidence
- OpenClaw: Building a Reviewer-Safe Paper Workflow for Embodied Manipulation

## Abstract support points

- Problem framing: manipulation systems often combine multiple components, making it easy to overstate capability without a clear evidence chain
- Project framing: OpenClaw is organized as a manipulation-focused system that may involve perception, planning, and action components
- Safe contribution framing: the work emphasizes a disciplined evidence workflow for system-level evaluation and paper preparation
- Limits: quantitative performance claims remain conditional on verified internal experiment artifacts

## 1. Introduction

- Motivate why manipulation system papers need clear evidence traceability
- Position OpenClaw as a system intended for manipulation tasks with multi-stage reasoning or execution requirements
- Explain that fair comparison requires benchmark-fit and baseline-fit checks, not just nearest-neighbor citations
- State contributions conservatively:
  - a structured evaluation and writing workflow
  - a claim-to-evidence mapping process
  - a benchmark and baseline validation procedure

## 2. Related Work

- System papers for manipulation and embodied tasks
- Planner-policy hybrids or adjacent control architectures
- Benchmarks and datasets used in comparable task settings
- Boundaries between direct baselines and adjacent non-comparable work

## 3. Method / System Overview

- OpenClaw system overview
- Main components and interfaces
- Design choices that can be tied to code, configs, or project notes
- Hypotheses that require ablation support

## 4. Experimental Setup

- Datasets and benchmarks with confirmed fit status
- Metrics and task definitions
- Baseline selection and why each baseline is fair
- Evaluation settings and protocol notes

## 5. Results

- Verified internal results only
- No unsupported numeric claims
- Tables and plots must map to identifiable runs or artifacts

## 6. Ablations And Analysis

- Component ablations across perception, planning, and control where relevant
- Failure modes and edge cases
- Partial evidence areas that should remain qualified in the text

## 7. Limitations

- missing benchmark-fit confirmations
- missing baseline coverage
- missing real-world validation if the current evidence is simulation-only
- missing ablation depth where system complexity is high

## 8. Conclusion

- Keep the conclusion narrow and evidence-backed
- Separate supported findings from future validation work

## Notes

- This outline is safe to use for planning
- It is not safe to convert partial claims into factual result statements without verified artifacts