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DOFBOT Pro📖
1. Get started quickly
  • 0.Unboxing introduction video
  • 1. First Trial
  • 2. Analysis of common problems
  • 3. Calibration offset
  • 4. Jetpack old system upgraded to SUPER system
  • 5. Precautions for DIY Robotic Arm
  • 6. Jetson Nano Version – Must Read
  • 7.Robotic arm gripping anomaly solutions
2. Assembly course
  • 1. Installation video
  • 2. Calibrate the Robotic Arm
  • 3. Install the map
3. Remote control course
  • 1. Mobile APP control
  • 2. USB handle control
  • 3. PC web page programming
4. Preparation
  • 1. Manage APP control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
  • 4. Modifying ROS_DOMAIN_ID for Multi-Machine
5. 2D visual tracking course
  • 1. Color block positioning
  • 2. Color block tracking
  • 3. Face positioning
  • 4. Face tracking
  • 5. Label code positioning
  • 6. Label code tracking
6. 2D visual grabbing course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Identify gesture grab color block
  • 8. Gesture stacking
  • 9.1 Single garbage sorting(Jetson-Nano)
  • 9.2 Single garbage sorting(Orin)
  • 10.1 Garbage sorting(Jetson-Nano)
  • 10.2 Garbage sorting(Orin)
  • 11. Tracking game
7. 2D Mediapipe course
  • 1. Hand detection
  • 2. Posture detection
  • 3. Overall detection
  • 4. Facial Detection
  • 5. Face Detection
  • 6. Face effects
  • 7. 3D Object Recognition
  • 8. Paintbrush
  • 9. Finger control
  • 10. Gesture Recognition
  • 11. Gesture control robotic arm action group
  • 12. Arm posture control robotic arm
  • 13. Palm Targeting
  • 14.1 Robotic arm tracks the palm(Jetson-Nano)
  • 14.2 Robotic arm tracks the palm(Orin)
  • 15. Fingertip trajectory recognition
  • 16. Fingertip trajectory control robotic arm
  • 17. Gesture Grasp and Release Objects
  • 18. Finger Control Robot Arm
8. AI Large Model Voice Module (Must Read)new
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
9. 2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.1 Garbage Recognition with Voice Broadcast(Jetson Nano)
  • 11.2 Garbage Recognition with Voice Broadcast(Orin)
  • 12.1 Garbage Sorting with Voice Broadcast(Jetson Nano)
  • 12.2 Garbage Sorting with Voice Broadcast(Orin)
10. 3D Depth Camera Course
  • 1. Dabai_DCW2 camera
  • 2. Deep Pseudo-Color Image
  • 3. Depth camera ranging
  • 4. Color block volume measurement
11. 3D Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
12. 3D Sorting and Grasping Course
  • 1. AprilTag ID sorting
  • 2. AprilTag abnormal height sorting
  • 3. MediaPipe Gesture-AprilTag ID sorting
  • 4. MediaPipe Gesture-AprilTag height sorting
  • 5. MediaPipe Gesture-AprilTag distance sorting
  • 6. Block color sorting
  • 7. Block abnormal height sorting
  • 8. Block different shape sorting
  • 9. Block shape abnormal height sorting
  • 10. Yolov11 garbage sorting(Orin)
13. 3D Tracking and Grasping Course
  • 1. AprilTag Tracking and Grasping
  • 2. Color Block Tracking and Grasping
  • 3. KCF Object Tracking and Grasping
14. 3D Voice Control for Grasping Course (Sorting & Tracking)
  • 1. Voice Control AprilTag ID Sorting
  • 2. Voice Control Remove Height Abnormal AprilTag
  • 3. Voice Control Tracking and Grasping AprilTag
  • 4. Voice Control Color Block Sorting
  • 5. Voice Control Remove Height Abnormal Color Block
  • 6. Voice Control Tracking and Grasping Color Block
  • 7. Voice Control Yolov11 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. AI Model Basicsnew
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
16. AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
17. AI Model – Text Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
18. AI Model – Voice Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
19. AI Model – Fun Applicationsnew
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
20. MoveIt Course
  • 1. MoveIt Configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
21. YOLOv11 Development Course (ORIN)
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Dataset Annotation
  • 10. Model Training_Conversion
  • 11. Model Prediction
22. YOLOv11 Development Course (Jetson Nano)
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Model Prediction
23. Linux Operating System
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
24. Basic Control Course
  • 1. Control RGB Light
  • 2. Control Buzzer
  • 3. Control Single Servo
  • 4. Read Servo Angle
  • 5. Control All Servos
  • 6. DOFBOT Up Down Left Right Movement
  • 7.DOFBOT dancing
  • 8. DOFBOT Memory Actions
  • 9. DOFBOT Grab Blocks
  • 10. Porter
  • 11. Stack Blocks
  • 12. Two DOFBOT Synchronized Actions
25. Jetson Board Basic Course
  • 1. Jetson board Introduction
  • 2. Write system image
  • 3. Re-write system image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
26. GPIO Hardware Control Course
Jetson Nano
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
Jetson Orin Nano_NX
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
27. OpenCV Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Picture cut
  • 8. Picture pan
  • 9. Picture mirroring
  • 10. Affine transformation
  • 11. Picture rotation
  • 12. Perspective transformation
  • 13. Grayscale processing
  • 14. Binary image
  • 15. Edge green detection
  • 16. Line segment drawing
  • 17. Rectangular circle drawing
  • 18. Text and picture drawing
28. Visual Basics Course
  • 1. Camera display
  • 2. Color Calibration
  • 3. Color recognition
  • 4. Face Detection
  • 5.1 Garbage Identification(Jetson-Nano)
  • 5.2 Garbage Identification(Orin)
  • 6.1 Object Recognition(Jetson-Nano)
  • 6.2 Object Recognition(Orin)
  • 7. Label code recognition
  • 8.PID algorithm basics
29. ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 Topic Communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
30. Visual Advanced Course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.ROS+Opencv basics
  • 6.AR QR code
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
31. Jupyter Lab Usage Course
  • 1. Read the document before operation
  • 2. Jupyter lab environment construction tutorial
  • 3. Basic tutorial of Jupyter lab
  • 4. Jupyter widgets configuration tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
Download
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  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_NANO4GB_SUB
  • VM_File
  • Code
  • Instruction_Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
  • Tools
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Welcome to DOFBOT Pro repository

9. Multimodal Large Model+Color Blocks Back In Place

Multimodal Large Model + Color Blocks Back In Place(Text Version)

Before running the function, you need to close the App and large programs. For the closing method, refer to [4. Preparation] - [1. Manage APP control services].

1. Function Description

After the program runs, first input the command to remember the current positions of color blocks in the terminal. After the program finishes recording, scramble the positions of the color blocks, then input the command to return the color blocks on the desktop to their original positions. After thinking, the large model will plan an action list, and the program will control the robotic arm to complete the color block return task according to the action functions in the action list.

The wooden blocks used in this lesson: 30x30x30mm blocks.

2. Startup

Users with Jetson-Nano board version need to enter the docker container and input the following command. Orin board users can directly open the terminal and input the following command:

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ros2 launch largemodel largemodel_control.launch.py text_chat_mode:=True

Then open a second terminal and input the following command:

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ros2 run text_chat text_chat

Now input the command to remember the current positions of color blocks in the terminal. You can refer to the following example:

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Remember the current positions of the color blocks

image-20251129151258160

After recording the color block positions, input the end task command in the terminal:

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End the position recording task

Then you can scramble the positions of the color blocks. After scrambling, input the color block return command in the terminal. You can refer to:

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Return the blocks on the desktop to their original positions

image-20251129154127570

After the buzzer beeps once, the program starts gripping color blocks and returning them to their original positions according to the order recorded in the program. If there are other color blocks above the current color block that needs to be returned, the upper color blocks will be removed first, then the target block will be gripped and returned to its original position.

3. Task Planning

3.1. Record Current Positions of Color Blocks

  1. Call seewhat() to observe the environment;

  2. Call seewhat() again to observe the environment, then determine which color block is the top block;

  3. If there are two or more piles of color blocks in the environment, based on whether it's a single block, choose to execute the following content, calculate for each pile of blocks, here only one action function can be output at a time:

    • If it's a single block, pass the outer border coordinates of the block to the function compute_pose(x1, y1, x2, y2, 'name'), where name represents the color of the block, values are red, blue, green or yellow;
    • If it's not a single block, pass the outer border coordinates of the top block to the function compute_pose_order(x1, y1, x2, y2, 'name', 'order'), where name represents the color of the block, values are red, blue, green or yellow, order represents the stacking order from top to bottom, values are strings composed of the first letters of each layer's color from top to bottom, for example if the stacking order from top to bottom is: red, blue, green, yellow, then the value of order is rbgy, and so on.
  4. If there's only one pile of blocks, pass the outer border coordinates of the top block to the function compute_pose_order(x1, y1, x2, y2, 'name', 'order'), where name represents the color of the block, values are red, blue, green or yellow, order represents the stacking order from top to bottom, values are strings composed of the first letters of each layer's color from top to bottom, for example if the stacking order from top to bottom is: red, blue, green, yellow, then the value of order is rbgy, and so on.

3.2. Color Blocks Return

  1. First, call the color_back_to_orin() function to control color blocks to return to original positions.
  2. Call the check_remove(color) function to check if the target color block exists in the removal list (where color is the color of the target block, value range is red, blue, green or yellow).
  3. If the target color block exists in the removal list, execute the following operations:
    (1) Call the grasp_from_rm_list(color) function to grip the target color block from the removal list (meaning of color parameter is the same as above);
    (2) Pass the color of the target block as parameter to the return_to_orin(color) function, for example when the target block is red, call return_to_orin(red) (value range of color parameter is the same as above).
  4. If the target color block does not exist in the removal list, follow these steps to return the block to its original position (only output one action function at a time):
    (1) Call the seewhat() function to find the current target block that needs to be returned;
    (2) Call the seewhat() function to determine if there are other objects above the current target block that needs to be returned;
    (3) If there are no other color blocks on top of the current target block that needs to be returned, execute the following steps:
    ① Call the grasp_obj(x1, y1, x2, y2) function to grip the current target block that needs to be returned (where (x1, y1, x2, y2) are the top surface border coordinates of the current target block that needs to be returned);
    ② Pass the color of the target block as parameter to the return_to_orin(color) function (value range of color parameter is the same as above).
    (4) If there are other color blocks on top of the current target block that needs to be returned, execute the following steps:
    ① Call the remove_obj(x1, y1, x2, y2, color) function to remove that block (where (x1, y1, x2, y2) are the top surface border coordinates of that block, color is the color of that block, value range is red, blue, green or yellow);
    ② Call the seewhat() function to observe the environment;
    ③ If there are no other color blocks above the current target block that needs to be returned, call the grasp_obj(x1, y1, x2, y2) function to grip the target block (where (x1, y1, x2, y2) are the top surface border coordinates of the target block);
    ④ Pass the color of the target block as parameter to the return_to_orin(color) function (value range of color parameter is the same as above)

4. Core Code Analysis

4.1. compute_pose_order Record Stacked Color Block Position

Source code path: LargeModel_ws/src/largemodel/largemodel/action_service.py

​x
#Parameter description
#x1,y1,x2,y2: Top outer border coordinates of the top color block, x1,y1 represent
#name: Color of the top color block
#order: Represents the stacking order list from top to bottom, for example if the stacked colors from top to bottom are red and yellow, then the passed order is ry
def compute_pose_order(self,x1,y1,x2,y2,name,order):
    #Get current color block parameters
    cur_name = name.strip("'\"")  
    color_order = order.strip("'\"")
    #Create a list to store the stacking order of color blocks from top to bottom
    order_list = list(color_order[1:])
    self.get_logger().info(f"order_list:{order_list}")
    self.cur_name = cur_name
    self.cur_pose[self.cur_name] = []
    #Start position calculation program
    cmd1 = "ros2 run largemodel_arm Record_pose"
    proc = subprocess.Popen(
        [
            "gnome-terminal",
            "--title=compute_pose",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )
    self.pid_num = proc.pid   
    self.get_logger().info(f"self.pid_num:{self.pid_num}")
    time.sleep(5.0)
    x1 = int(x1)
    y1 = int(y1)
    x2 = int(x2)
    y2 = int(y2)
    self.object_position_pub.publish(Int16MultiArray(data=[x1, y1, x2, y2]))
​
    while not self.compute_pose_future.done():
        if self.interrupt_flag:
            self.check_close_compute_pose()
            Arm.Arm_serial_servo_write6(90,150,12,20,90,30,1000)
            self.stop()
            return
        time.sleep(0.1)
​
    result = self.compute_pose_future.result()
    self.check_close_compute_pose()
    if not self.interrupt_flag:
        if result.data == "compute_pose_done":
            for i in range(0,len(order_list)):
                #Here we consider that xy directions are consistent with the top block, only height changes, so just need to change z value, decreasing by 2.8cm each time, approximately the height of one color block
                x = self.cur_pose[cur_name][0]
                y = self.cur_pose[cur_name][1]
                z = self.cur_pose[cur_name][2] - (i+1)*0.028
                if order_list[i] == 'r' or  order_list[i] == 'R':
                    self.cur_pose['red'] = []
                    self.cur_pose['red'].append(x) 
                    self.cur_pose['red'].append(y) 
                    self.cur_pose['red'].append(z) 
                elif order_list[i] == 'b' or  order_list[i] == 'B':
                    self.cur_pose['blue'] = []
                    self.cur_pose['blue'].append(x) 
                    self.cur_pose['blue'].append(y) 
                    self.cur_pose['blue'].append(z)                
                elif order_list[i] == 'y' or  order_list[i] == 'Y':
                    self.cur_pose['yellow'] = []
                    self.cur_pose['yellow'].append(x) 
                    self.cur_pose['yellow'].append(y) 
                    self.cur_pose['yellow'].append(z)  
                elif order_list[i] == 'g' or  order_list[i] == 'G':
                    self.cur_pose['green'] = []
                    self.cur_pose['green'].append(x) 
                    self.cur_pose['green'].append(y) 
                    self.cur_pose['green'].append(z)  
                    
            self.get_logger().info(f"self.cur_pose:{self.cur_pose}")
            #Feedback to large model that recording stacked color block positions is complete
            self.action_status_pub("compute_pose_order_done", x1=x1, y1=y1, x2=x2, y2=y2,name=name,order=order)
        else:
            self.action_status_pub("compute_pose_failed", x1=x1, y1=y1, x2=x2, y2=y2)

4.2. color_back_to_orin Publish Color Block Return Order

Source code path: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def color_back_to_orin(self):
    self.back_list = True
    #Buzzer beeps once
    Arm.Arm_Buzzer_On()
    time.sleep(0.5)
    Arm.Arm_Buzzer_Off()
    #Traverse the list storing color block positions in reverse order, feedback to large model the current color block return instruction
    for key in reversed(self.cur_pose.keys()):
        if key == 'red':
            self.action_status_pub("send_red_back_to_orin")
        elif  key == 'green':
            self.action_status_pub("send_green_back_to_orin")
        elif key == 'blue':
            self.action_status_pub("send_blue_back_to_orin")
        elif key == 'yellow':
            self.action_status_pub("send_yellow_back_to_orin") 
        while not self.return_done:
            time.sleep(0.1)
        self.return_done = False
    self.action_status_pub("return_to_orin_done")
    self.back_list = False
"send_red_back_to_orin": "Robot feedback: Return the red block to its original position",
"send_green_back_to_orin": "Robot feedback: Return the green block to its original position",
"send_blue_back_to_orin": "Robot feedback: Return the blue block to its original position",
"send_yellow_back_to_orin": "Robot feedback: Return the yellow block to its original position"

4.3. check_remove(color) Check if Current Target Color Block Exists in Removal List

Source code path: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def check_remove(self,color):
    #Get current color block color
    check_color = color.strip("'\"")
    #If current color block color can be found in removal list, then feedback to large model that current color block can be found in removal list
    if check_color in self.cur_rm_pose:
        self.action_status_pub("color is in the rm_list")
    else:
        self.action_status_pub("color is not in the rm_list")
        
"color is in the rm_list" : "Color block exists in removal list, grip this color block from removal list",
"color is not in the rm_list" : "Color block does not exist in removal list, determine if there are other objects on top of this color block",

4.4. grasp_from_rm_list Grip Color Block from Removal List

Source code path: LargeModel_ws/src/largemodel/largemodel/action_service.py

def grasp_from_rm_list(self,color):
    #Get current color block color
    tar_color = color.strip("'\"")
    #Find corresponding robotic arm posture value in removal dictionary based on current color
    tar_joints = self.cur_rm_pose.get(tar_color)
    Arm.Arm_serial_servo_write6(tar_joints[0], tar_joints[1], tar_joints[2], tar_joints[3], tar_joints[4], tar_joints[5],2000)
    time.sleep(2.0)
    Arm.Arm_serial_servo_write(6, 140, 1000)
    time.sleep(2.0)
    Arm.Arm_serial_servo_write6(90,120,10,10,90,140,2000)
    time.sleep(2.0)
    #Feedback to large model that gripping from removal list is complete
    self.action_status_pub("grasp_from_rm_list_done")
Multimodal Large Model+robotic arm handling
Multimodal Large Model + Intent Prediction + Robotic Arm Gripping

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