1-analyze-task

name	1-analyze-task
description	First analyze the task and generate a collect/reset plan, then loop collect + reset until done. Trigger when the user mentions "analyze task", "step breakdown", or "learn a task".

1-analyze-task

This skill turns a natural-language request into a two-phase workflow: Phase A (task analysis and plan generation) and Phase B (automated collect and environment reset loop). Execute strictly in order.

Set $ZERO2SKILL_ROOT to this repository root. Three-view scripts and analysis results live under skills/ in this repo.

Workflow

Phase A: Task Analysis and Plan Generation

1. Scene perception: Call $ZERO2SKILL_ROOT/skills/understand-three-view-images/scripts/auto_understand_images.sh --prompt "Briefly describe the current desktop scene, including which objects are present, their positions, and their states" to extract current scene and object information.
2. Confirm goals: Combine the task description with available skills (e.g. grasp-tool) to define the action goals and skill invocation parameters needed to complete the task.
3. Plan design:
   • Collection plan: Design the full action chain for one collection round and the Shell commands (collection_command).
   • Collection success criteria: Define clear success conditions and a judging prompt.
   • Reset plan: Design the action chain that restores the environment to the pre-collection state and the Shell commands (reset_command).
   • Reset success criteria: Define clear success conditions and a judging prompt.
4. Save and confirm (gate): Write the analysis strictly in the [Analysis Result Format] to the analysis result path. When done, must report the plan to the user so they can confirm and fine-tune it, and ask whether to continue. Do not enter Phase B until the user explicitly agrees.

Phase B: Automated Collect and Reset Loop

Prerequisite: Phase A is complete and the user has explicitly confirmed.

1. Read the plan: Read analyze_result.yaml for all commands, prompt_for_judging_collect, prompt_for_judging_reset, and the total number of episodes to collect (need_collect_count).
2. Loop: While current_collect_count < need_collect_count, run one round:
   • Collection actions: First observe to get fresh scene info, then execute Shell commands in collection_command one by one.
   • Judge collection: Call $ZERO2SKILL_ROOT/skills/understand-three-view-images/scripts/auto_understand_images.sh --prompt <prompt_for_judging_collect>:
     • On success, proceed to reset.
     • On failure, retry from collection judging; if collect_attempts > 3, pause and request human help.
   • Reset actions: First observe to get fresh scene info, then execute Shell commands in reset_command one by one.
   • Judge reset: Call $ZERO2SKILL_ROOT/skills/understand-three-view-images/scripts/auto_understand_images.sh --prompt <prompt_for_judging_reset>:
     • On success, proceed to the next round.
     • On failure, re-run reset actions; if reset_attempts > 3, pause and request human help.
3. In Phase B, keep user messages brief: omit non-essential info; only report the current round, collection result, reset result, and progress.

Example

User task: "Clean the desktop (target: collect 50 episodes)"

Phase A:

1. The system finds an apple, a storage box, etc. on the table.
2. Plan: collection goal is "call grasp-tool to put the apple into the storage box"; reset goal is "call grasp-tool to take the apple out of the box and place it randomly on the table".
3. Success criteria: collection succeeds when the apple is in the box; reset succeeds when the apple is back on the table.
4. Save the plan to analyze_result.yaml and ask the user: "Analysis complete. Will run grasp-tool collection for ten rounds. Confirm to start?" Phase B (starts after the user replies "confirm"):
5. Read the plan and start round 1 collection.
6. Re-perceive scene -> put into container -> wait -> visual judge (success).
7. Re-perceive scene -> take out and reset -> wait -> visual judge (success).
8. Auto-loop until 50 rounds finish or an exception occurs.

Output Format

Analysis result storage

• Path: $ZERO2SKILL_ROOT/skills/self-learning/analyze_result.yaml
• Example: see analyze_result.example.yaml in the same directory
• Analysis result format:

task_name: <name of the task being analyzed>
dataset_dir: <root directory for dataset storage>
need_collect_count: <total number of episodes to collect>
task_description: <understanding of the task and extra notes>
collection_plan: <natural-language collection plan, including skill call sequence and parameter design>
- step_1: <which skill to call first, with what parameters, and expected result>
collection_command: <shell commands for the collection plan>
- step_1: <first shell command, including skill invocation and parameters>
reset_plan: <natural-language reset plan>
- step_1: <which skill to call first, with what parameters, and expected result>
reset_command: <shell commands for the reset plan>
- step_1: <first shell command, including skill invocation and parameters>
collect_success_criteria: <how to judge collection success / task completion>
prompt_for_judging_collect: <prompt asking whether collection succeeded>
reset_success_criteria: <how to judge reset success / environment restored>
prompt_for_judging_reset: <prompt asking whether reset succeeded>

Note: Do not stop until all data has been collected.

Handling Out-of-Distribution (OOD) cases

Any situation not covered in this document is OOD. See skills/self-learning/reference/OOD_handling_flow.md: pause and ask the user for help; do not change the plan or system environment on your own.

File layout

$ZERO2SKILL_ROOT/skills/self-learning/
├── analyze_result.yaml           # Phase A output / Phase B input (local, not committed)
├── analyze_result.example.yaml
└── reference/
    └── OOD_handling_flow.md