메뉴
Use for robot control nodes in dora. Triggers on: dora-piper, dora-reachy2, dora-ugv, dora-kit-car, dora-rdt-1b, Piper, Reachy, UGV, robot arm, chassis, VLA, robot control, Agilex, joint control, kinematics, teleoperation, 机械臂, 底盘, 机器人控制, 遥操作
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use for dora CLI command questions. Triggers on: dora run, dora build, dora start, dora stop, dora new, dora up, dora destroy, dora list, dora ps, dora logs, dora check, dora graph, dora daemon, dora coordinator, CLI命令, 命令行, 运行数据流, 构建, 启动, 停止
Use for data recording and replay questions with dora. Triggers on: recording, replay, lerobot, data collection, dataset, rosbag, record data, play back, training data, HDF5, parquet, 数据记录, 数据回放, 数据收集, 训练数据
Use for dora-rs dataflow YAML configuration questions. Triggers on: dataflow.yml, dataflow.yaml, nodes:, inputs:, outputs:, timer, dora/timer, queue_size, env:, build:, path:, git:, branch:, tag:, restart_policy, send_stdout_as, operator:, operators:, YAML配置, 数据流配置, 节点配置, 输入输出, 定时器
Use for audio processing pipeline questions with dora. Triggers on: speech, TTS, STT, voice, audio, Whisper, Kokoro, text-to-speech, speech-to-text, voice activity, VAD, microphone, 语音, 音频, 文本转语音, 语音转文本, 语音识别
Use for robot control pipeline questions with dora. Triggers on: robot, arm, chassis, actuator, servo, motor, gripper, kinematics, FK, IK, URDF, joint, position control, velocity control, serial port, 机器人, 机械臂, 底盘, 执行器, 舵机, 电机, 运动学
Use for ML/Vision pipeline questions with dora. Triggers on: YOLO, detection, object detection, segmentation, tracking, VLM, SAM, camera, webcam, image processing, bbox, bounding box, rerun, visualization, 视觉, 目标检测, 分割, 跟踪, 摄像头, 图像处理
| name | hub-robot |
| description | Use for robot control nodes in dora. Triggers on: dora-piper, dora-reachy2, dora-ugv, dora-kit-car, dora-rdt-1b, Piper, Reachy, UGV, robot arm, chassis, VLA, robot control, Agilex, joint control, kinematics, teleoperation, 机械臂, 底盘, 机器人控制, 遥操作 |
| globs | ["**/dataflow.yml","**/dataflow.yaml"] |
| source | https://github.com/dora-rs/dora-hub |
Robot arms, chassis, and vision-language-action models
| Node | Install | Description | Type |
|---|---|---|---|
| dora-piper | pip install dora-piper | Agilex Piper arm | Arm |
| dora-reachy2 | pip install dora-reachy2 | Pollen Reachy 2 humanoid | Humanoid |
| dora-ugv | pip install dora-ugv | Agilex UGV chassis | Chassis |
| dora-kit-car | pip install dora-kit-car | Open-source chassis | Chassis |
| dora-rdt-1b | pip install dora-rdt-1b | Robotic Diffusion Transformer | VLA |
Agilex Piper robot arm control.
Install Agilex Piper SDK:
git clone https://github.com/agilexrobotics/piper_sdk
cd piper_sdk
# Follow setup and installation instructions
Ensure CAN bus is activated and leader arms are not connected.
- id: piper
build: pip install dora-piper
path: dora-piper
inputs:
joint_positions: policy/positions # Float32Array joint targets
outputs:
- joint_state # Current joint positions
Pollen Robotics Reachy 2 humanoid robot.
- id: reachy
build: pip install dora-reachy2
path: dora-reachy2
inputs:
command: control/command
outputs:
- image_left # Left camera image
- image_right # Right camera image
- joint_state # Joint positions
Agilex UGV (Unmanned Ground Vehicle) control.
- id: ugv
build: pip install dora-ugv
path: dora-ugv
inputs:
velocity: control/velocity # [linear_x, angular_z]
outputs:
- odometry # Position and velocity feedback
Open-source educational chassis.
- id: car
build: pip install dora-kit-car
path: dora-kit-car
inputs:
command: control/command
Robotic Diffusion Transformer for policy inference from images and language.
- id: policy
build: pip install dora-rdt-1b
path: dora-rdt-1b
inputs:
image: camera/image
instruction: input/text # Natural language instruction
outputs:
- action # Joint positions/velocities
nodes:
# Camera
- id: camera
build: pip install opencv-video-capture
path: opencv-video-capture
inputs:
tick: dora/timer/millis/100
outputs:
- image
# Microphone for voice commands
- id: microphone
build: pip install dora-microphone
path: dora-microphone
inputs:
tick: dora/timer/millis/100
outputs:
- audio
# Speech to text
- id: whisper
build: pip install dora-distil-whisper
path: dora-distil-whisper
inputs:
input: microphone/audio
outputs:
- text
# VLA policy
- id: policy
build: pip install dora-rdt-1b
path: dora-rdt-1b
inputs:
image: camera/image
instruction: whisper/text
outputs:
- action
# Robot arm
- id: arm
build: pip install dora-piper
path: dora-piper
inputs:
joint_positions: policy/action
outputs:
- joint_state
# Visualization
- id: rerun
build: pip install dora-rerun
path: dora-rerun
inputs:
image: camera/image
joint_state:
source: arm/joint_state
metadata:
primitive: "jointstate"
nodes:
# Leader arm (human operated)
- id: leader
build: pip install dora-piper
path: dora-piper
inputs:
tick: dora/timer/millis/20
outputs:
- joint_state
env:
MODE: leader
CAN_INTERFACE: can0
# Follower arm (robot)
- id: follower
build: pip install dora-piper
path: dora-piper
inputs:
joint_positions: leader/joint_state
outputs:
- joint_state
env:
MODE: follower
CAN_INTERFACE: can1
# Camera for recording
- id: camera
build: pip install opencv-video-capture
path: opencv-video-capture
inputs:
tick: dora/timer/millis/33
outputs:
- image
# Recording for imitation learning
- id: recorder
build: pip install llama-factory-recorder
path: llama-factory-recorder
inputs:
image_right: camera/image
ground_truth: leader/joint_state
outputs:
- text
import pyarrow as pa
import numpy as np
# 7-DOF arm joint positions (radians)
joint_positions = np.array([0.0, -0.5, 0.0, 1.0, 0.0, 0.5, 0.0], dtype=np.float32)
# Send joint command
node.send_output("joint_positions", pa.array(joint_positions), {
"num_joints": 7,
"primitive": "jointstate"
})
if event["type"] == "INPUT":
joint_state = event["value"].to_numpy()
print(f"Joint positions: {joint_state}")
# 7 values: [x, y, z, qx, qy, qz, qw]
pose = np.array([0.5, 0.0, 0.3, 0.0, 0.0, 0.0, 1.0], dtype=np.float32)
node.send_output("pose", pa.array(pose), {"primitive": "pose"})
nodes:
# Camera
- id: camera
build: pip install opencv-video-capture
path: opencv-video-capture
inputs:
tick: dora/timer/millis/33
outputs:
- image
# Object detection
- id: yolo
build: pip install dora-yolo
path: dora-yolo
inputs:
image: camera/image
outputs:
- bbox
# Navigation control (custom node)
- id: navigator
path: navigator.py
inputs:
detections: yolo/bbox
outputs:
- velocity
# Chassis
- id: ugv
build: pip install dora-ugv
path: dora-ugv
inputs:
velocity: navigator/velocity
outputs:
- odometry
# Visualization
- id: rerun
build: pip install dora-rerun
path: dora-rerun
inputs:
image: camera/image
detections: yolo/bbox
- id: rerun
build: pip install dora-rerun
path: dora-rerun
inputs:
jointstate_piper: arm/joint_state
env:
piper_urdf: /path/to/piper.urdf
piper_transform: "0 0.3 0" # x y z offset
Note: URDF file paths in the URDF are relative to the dataflow working directory.