Update README.md
Browse filesUpdate README.md to add detailed task descriptions
README.md
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@@ -18,6 +18,28 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
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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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- **Homepage:** [More Information Needed]
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- **Paper:** [More Information Needed]
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- **License:** apache-2.0
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- **Task Objective:** Place the OAK-D-LITE camera into the mounting case and secure the lid.
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- **Operational Objects:** An OAK-D-LITE camera along with its original complete packaging (Purchase link: [【淘宝】7天无理由退货 https://e.tb.cn/h.606wnDDmrTMhcch?tk=Dr7feJHVOyp CZ009 「OpenCV AI视觉套件 OAK-D-Lite智能开发 深度摄像头RGB高清相机」点击链接直接打开 或者 淘宝搜索直接打开])
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- **Operation Duration:** Each operation takes approximately 40 seconds.
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- **Recording Frequency:** 30 Hz.
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- **Robot Type:** 7-DOF dual-arm G1 robot.
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- **End Effector:** Three-fingered dexterous hands.
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- **Dual-Arm Operation:** Yes.
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- **Image Resolution:** 640x480.
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- **Camera Positions:** Wrist-mounted (monocular camera) + head-mounted (binocular cameras).
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- **Data Content:**
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- • Robot's current state.
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- • Robot's next action.
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- • Current camera view images.
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- **Robot Initial Posture:** The first robot state in each dataset entry.
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- **Object Placement:** Position the camera case at the midpoint between the robot's arms in their initial state (arms vertical), and 20cm away from the inner edge of the table's wall (robot-facing side).
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- **Camera View:** Follow the guidelines in **Part 5** of [AVP Teleoperation Documentation](https://github.com/unitreerobotics/avp_teleoperate).
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- **Important Notes:**
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1. Due to the inability to precisely describe spatial positions, adjust the scene to closely match the first frame of the dataset after installing the hardware as specified in **Part 5** of [AVP Teleoperation Documentation](https://github.com/unitreerobotics/avp_teleoperate).
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2. Data collection is not completed in a single session, and variations between data entries exist. Ensure these variations are accounted for during model training.
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## Dataset Structure
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