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Robotwin-demo-clean-50-artifacts

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Tags:
robotwin
embodied-ai
robotics
bimanual-manipulation
evaluation
video
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Upload RoboTwin evaluation artifacts

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  1. README.md +58 -0
  2. cross_configuration/_result.txt +5 -0
  3. cross_configuration/episode0.mp4 +3 -0
  4. cross_configuration/episode1.mp4 +3 -0
  5. cross_configuration/episode10.mp4 +3 -0
  6. cross_configuration/episode11.mp4 +3 -0
  7. cross_configuration/episode12.mp4 +3 -0
  8. cross_configuration/episode13.mp4 +3 -0
  9. cross_configuration/episode14.mp4 +3 -0
  10. cross_configuration/episode15.mp4 +3 -0
  11. cross_configuration/episode16.mp4 +3 -0
  12. cross_configuration/episode17.mp4 +3 -0
  13. cross_configuration/episode18.mp4 +3 -0
  14. cross_configuration/episode19.mp4 +3 -0
  15. cross_configuration/episode2.mp4 +3 -0
  16. cross_configuration/episode20.mp4 +3 -0
  17. cross_configuration/episode21.mp4 +3 -0
  18. cross_configuration/episode22.mp4 +3 -0
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  21. cross_configuration/episode25.mp4 +3 -0
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  24. cross_configuration/episode28.mp4 +3 -0
  25. cross_configuration/episode29.mp4 +3 -0
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README.md ADDED
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+ ---
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+ language:
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+ - zh
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+ pretty_name: RoboTwin beat_block_hammer demo_clean-50 artifacts
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+ tags:
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+ - robotwin
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+ - embodied-ai
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+ - robotics
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+ - bimanual-manipulation
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+ - evaluation
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+ - video
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+ license: other
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+ ---
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+
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+ # RoboTwin beat_block_hammer demo_clean-50 Artifacts
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+
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+ 这个仓库存放的是一次 RoboTwin 单任务闭环实验的关键结果产物，而不是官方完整数据集。
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+
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+ ## 仓库内容
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+
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+ - `same_distribution/`
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+ - `demo_clean -> demo_clean` 的评测结果与 100 个 rollout 视频
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+ - `cross_configuration/`
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+ - `demo_clean -> demo_randomized` 的评测结果与 100 个 rollout 视频
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+ - `experiment_summary.json`
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+
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+ ## 已完成的闭环
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+
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+ 本实验完整跑通了以下链路：
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+
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+ 1. RoboTwin 官方任务程序生成专家轨迹
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+ 2. 原始轨迹落盘为 raw data
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+ 3. ACT 预处理生成 training-ready data
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+ 4. ACT 正式训练
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+ 5. 策略重新部署回 RoboTwin 环境
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+ 6. 在 clean / randomized 两种配置下正式评测
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+
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+ ## 关键结果
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+
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+ - 同分布：`demo_clean -> demo_clean` 成功率 `0.64`
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+ - 跨配置：`demo_clean -> demo_randomized` 成功率 `0.0`
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+
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+ ## 工程心得
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+
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+ 这次实验说明，RoboTwin 的价值不只是“能产出模拟数据”，而是它把具身智能实验做成了一条严格的工程流水线：
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+
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+ - 环境层定义任务、对象和成功标准
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+ - 专家层生成并筛选成功轨迹
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+ - 数据层保存多模态原始观测
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+ - 训练层把原始数据转成策略学习输入
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+ - 部署层把策略重新接回环境
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+ - 评测层用统一协议统计成功率并保存视频证据
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+
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+ 从研究角度看，这组结果也很典型：clean 分布上的成功并不自动意味着对 domain randomization 的鲁棒性。RoboTwin 2.0 所强调的强随机化、数据多样性和稳健评测，在这次实验里都得到了非常具体的体现。
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+
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+ ## 说明
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+
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+ 本仓库中的产物由 RoboTwin 官方代码仓库生成。上游代码、任务定义与论文请参考 RoboTwin 官方项目。
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