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README.md
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---
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license:
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task_categories:
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- robotics
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tags:
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- 장면 판 `props-8`, 규격 판 `2`, 꾸러미 판 `0.7.0-draft`(셋 중 하나라도 다르면 다른 데이터다).
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- 실험 현상 층(구리 석출·용액 색, 막대 박, 광물 거품·쇳가루·조흔 줄) 없이 그린 그림이다(장면 판이 켜지 않는다).
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- 시작 자세의 출처 녹화: [`leapshared/Leaf_rules_20261022_155631`](https://huggingface.co/datasets/leapshared/Leaf_rules_20261022_155631)@`bca9fcbfacef7fad2d16ecf5535c669006e74413`.
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- 생성 코드: 저장소 커밋 `
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## 생성기
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## 라이선스
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**선생님
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## 판(리비전) 고정
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Training demonstrations of SciEdu-Bench (MuJoCo), experiment `leaf`, scene `props-8`, spec `2`, package `0.7.0-draft`. 150 episodes made in simulation by scene_code (150); no recording replay. Only judged successes are kept. The training starts are disjoint from the package's fixed evaluation starts (same-task recorded-episode overlap 0, starts within 0.5 cm and 5.0 degrees of one 0). Schema: the real recordings' (30 fps, 16 absolute joint degrees, three 480x640 AV1 cameras); `task` is the benchmark sentence without the recordings' head. License:
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- **Homepage:** [More Information Needed]
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- **Paper:** [More Information Needed]
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- **License:**
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## Dataset Structure
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license: apache-2.0
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task_categories:
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- robotics
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tags:
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- 장면 판 `props-8`, 규격 판 `2`, 꾸러미 판 `0.7.0-draft`(셋 중 하나라도 다르면 다른 데이터다).
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- 실험 현상 층(구리 석출·용액 색, 막대 박, 광물 거품·쇳가루·조흔 줄) 없이 그린 그림이다(장면 판이 켜지 않는다).
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- 시작 자세의 출처 녹화: [`leapshared/Leaf_rules_20261022_155631`](https://huggingface.co/datasets/leapshared/Leaf_rules_20261022_155631)@`bca9fcbfacef7fad2d16ecf5535c669006e74413`.
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- 생성 코드: 저장소 커밋 `fefae33f60e1ff4bb3a6d7a74ad4bb5afaa27abc`, 도구 `scripts/sim/bench_train_data.py`, 짓는 법 `config/bench/sciedu_bench_train_data.yaml`.
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## 생성기
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## 라이선스
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**apache-2.0**(허브 메타데이터 `apache-2.0`, 선생님 2026-11-11, 실물 녹화 leapshared/SciEdu_mineral_steps_20261109와 같게). 이 시뮬 시연 데이터셋의 라이선스다. 코드·장면(소품 CAD·사진 결)의 라이선스는 꾸러미의 `license` 칸에서 따로 정한다.
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## 판(리비전) 고정
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---
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Training demonstrations of SciEdu-Bench (MuJoCo), experiment `leaf`, scene `props-8`, spec `2`, package `0.7.0-draft`. 150 episodes made in simulation by scene_code (150); no recording replay. Only judged successes are kept. The training starts are disjoint from the package's fixed evaluation starts (same-task recorded-episode overlap 0, starts within 0.5 cm and 5.0 degrees of one 0). Schema: the real recordings' (30 fps, 16 absolute joint degrees, three 480x640 AV1 cameras); `task` is the benchmark sentence without the recordings' head. License: apache-2.0 (metadata `apache-2.0`). Drawn without the phenomenon layer (the scene does not turn it on). Pin a commit (`--dataset.revision`). Leaf sorting scene (props-8, MuJoCo, both hands grasp physically): a card standing on the middle line goes to the left side (left arm) or the right side (right arm), then the arm returns. The two single tasks share their starts. With only twenty demo-share recorded episodes, each is drawn several times with another card, so card places repeat. The long task (leaf_script) sorts the cards one after another, each stood in the middle in turn, and the cards already placed must stay standing on their side. Every demo-share recording holds one card, so its demo starts chain several demo-share first cards (4 to 10, drawn from the eval starts' counts); its eval starts take one recording's cards in order.
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- **Homepage:** [More Information Needed]
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- **Paper:** [More Information Needed]
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- **License:** apache-2.0
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## Dataset Structure
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