idea-discovery-robot

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 1cf95b4becf0 · 31 Oct 2026Report an issue or request a re-scan

• Publisherwanshuiyin GitHub userOwner of the repository this skill is read from
• Source folderwanshuiyin/auto-claude-code-research-in-sleep/skills/idea-discovery-robotThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict1cf95b4becf0A new audit runs when the source changes
• Repository statusNot archivedLast push 1 day ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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,…

---
name: idea-discovery-robot
description: "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."
argument-hint: "[robotics-direction]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Skill, mcp__codex__codex, mcp__codex__codex-reply
---

# Robotics Idea Discovery Pipeline

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.


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-6-astra`** — External reviewer model via Codex MCP
- **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:

- **Embodiment**
- **Task family**
- **Environment type**: tabletop, warehouse, home, outdoor, aerial, driving, legged terrain
- **Observation modalities**
- **Action interface / controller abstraction**
- **Learning regime**: RL, imitation, behavior cloning, world model, planning, VLA/VLM, classical robotics, hybrid
- **Available assets**: simulator, benchmark suite, teleop data, offline logs, existing codebase, real hardware
- **Compute budget**
- **Safety constraints**
- **Desired contribution type**: method, benchmark, diagnosis, systems, sim2real, data curation

If some fields are missing, make explicit assumptions and default to:
- **simulation-first**
- **public benchmark preferred**
- **no real robot execution**

Write this frame into working notes before moving on. Every later decision should reference it.

## Phase 1: Robotics Literature Survey

Invoke:

```
/research-lit "$ARGUMENTS — focus venues: CoRL, RSS, ICRA, IROS, RA-L, TRO, Science Robotics"
```

Then reorganize the findings using a robotics lens instead of a generic ML lens.

### Build a Robotics Landscape Matrix

For each relevant paper, classify:

| Axis | Examples |
|------|----------|
| Embodiment | single-arm, mobile manipulator, humanoid, drone, quadruped |
| Task | pick-place, insertion, navigation, locomotion, long-horizon rearrangement |
| Learning setup | RL, BC, IL, offline RL, world model, planning, diffusion policy |
| Observation | RGB, RGB-D, proprioception, tactile, language |
| Action abstraction | torque, joint velocity, end-effector delta pose, waypoint planner |
| Eval regime | pure sim, sim+real, real-only, offline benchmark |
| Benchmark | ManiSkill, RLBench, Isaac Lab, Habitat, Meta-World, CALVIN, LIBERO, custom |
| Metrics | success rate, collision rate, intervention count, path length, latency, energy |
| Main bottleneck | sample inefficiency, brittleness, reset cost, perception drift, sim2real gap |

### Search Priorities

When refining the survey, prioritiz

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source 1cf95b4becf0

Questions about idea-discovery-robot

• github.com/wanshuiyin/auto-claude-code-research-in-sleep/tree/ma — the audited source at commit 1cf95b4becf0 and every finding aboveObserved · 31 Oct 2026

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