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Idea Discovery Robot

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says \"robotics idea discovery\", \"机器人找idea\", \"embodied AI idea\", \"机器人方向探索\", \"sim2real 选题\", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

Data, AI & Research|v1|Updated 7/2/2026|GitHub source
MCP get_skill({ skillId: "idea-discovery-robot-78d4af99" })

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# Robotics Idea Discovery Pipeline

Orchestrate a robotics-specific idea discovery workflow for: **$ARGUMENTS**

## Overview

This skill chains four sub-skills into a single automated pipeline:

```
/research-lit → /idea-creator (robotics framing) → /novelty-check → /research-review
  (survey)              (filter + pilot plan)         (verify novel)    (critical feedback)
```

But every phase must be grounded in robotics-specific constraints:
- **Embodiment**: arm, mobile manipulator, drone, humanoid, quadruped, autonomous car, etc.
- **Task family**: grasping, insertion, locomotion, navigation, manipulation, rearrangement, multi-step planning
- **Observation + action interface**: RGB/RGB-D/tactile/language; torque/velocity/waypoints/end-effector actions
- **Simulator / benchmark availability**: simulation-first by default
- **Real robot constraints**: hardware availability, reset cost, safety, operator time
- **Evaluation quality**: success rate plus failure cases, safety violations, intervention count, latency, sample efficiency
- **Sim2real story**: whether the idea can stay in sim, needs offline logs, or truly requires hardware

The goal is not to produce flashy demos. The goal is to produce ideas that are:
- benchmarkable
- falsifiable
- feasible with available robotics infrastructure
- interesting even if the answer is negative

## Constants

- **MAX_PILOT_IDEAS = 3** — Validate at most 3 top ideas deeply
- **PILOT_MODE = `sim-first`** — Prefer simulation or offline-log pilots before any hardware execution
- **REAL_ROBOT_PILOTS = `explicit approval only`** — Never assume physical robot access or approval
- **AUTO_PROCEED = true** — If user does not respond at checkpoints, proceed with the best sim-first option
- **REVIEWER_MODEL = `gpt-5.5`** — External reviewer model via a secondary Codex agent
- **TARGET_VENUES = CoRL, RSS, ICRA, IROS, RA-L** — Default novelty and reviewer framing

> Override inline, e.g. `/idea-discovery-robot "bimanual manipulation" — only sim ideas, no real robot` or `/idea-discovery-robot "drone navigation" — focus on CoRL/RSS, 2 pilot ideas max`

## Execution Rule

Follow the phases in order. Do **not** stop after a checkpoint unless:
- the user explicitly says to stop, or
- the user asks to change scope and re-run an earlier phase

If `AUTO_PROCEED=true` and the user does not respond, continue immediately to the next phase using the strongest **sim-first, benchmark-grounded** option.

## Phase 0: Frame the Robotics Problem

Before generating ideas, extract or infer this **Robotics Problem Frame** from `$ARGUMENTS` and local project context:

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#broad-capability#wanshuiyin-aris#ml-research#autonomous#deep#researchcodex-cliopenai-apifilesystem-access