Spot-Sim2Real is a modular library for development of Spot for embodied AI tasks (e.g., Language-guided Skill Coordination (LSC), Adaptive Skill Coordination (ASC)) -- configuring Spot robots, controlling sensorimotor skills, and coordinating Large Language Models (LLMs).
Please refer to the setup instructions page for information on how to setup the repo. Note that this repo by-default does not track dirty status of submodules, if you're making any intentional changes within the third-party packages be sure to track them separately.
Computer can be connected to the robot in one of the following modes.
- Ethernet (Gives best network speed, but it is cluttery :sad: )
This mode can be used to create a wired connection with the robot. Useful for teleoperating the robot via computer - Access Point Mode
This is a wireless mode where robot creates its wifi network. Connect robot to this mode for teleoperating it using controller over long distances. Robot is in Access Point mode if you see a wifi with name likespot-BD-***********(where * is a number) - Client Mode (Gives 2nd best network speed, we usually prefer this)
This is a wireless mode where robot is connected to an external wifi network (from a nearby router). Computer should be connected to this same network, wired connection between router and computer will be faster than wireless connection.
Follow the steps from Spot's Network Setup page by Boston Dynamics to connect to the robot.
After setting up spot in correct network configuration, please add its IP inside bashrc
echo "export SPOT_IP=<spot's ip address>" >> ~/.bashrc
source ~/.bashrcTest and ensure you can ping spot
ping $SPOT_IPIf you get response like this, then you are on right network
(spot_ros) user@linux-machine:~$ ping $SPOT_IP
PING 192.168.1.5 (192.168.1.5) 56(84) bytes of data.
64 bytes from 192.168.1.5: icmp_seq=1 ttl=64 time=8.87 ms
64 bytes from 192.168.1.5: icmp_seq=2 ttl=64 time=7.36 msBefore starting to run the code, you need to ensure that all ROS env variables are setup properly inside bashrc. Please follow the steps from Setting ROS env variables for proper ROS env var setup.
Go to the repository
cd /path/to/spot-sim2real/The code for the demo lies inside the main branch.
# Check your current git branch
git rev-parse --abbrev-ref HEAD
# If you are not in the `main` branch, then checkout to the `main` branch
git checkout main- Since we do not have a physical emergency stop button (like the large red push buttons), we need to run an e-stop node.
python -m spot_wrapper.estop
- Keep this window open at all the times, if the robot starts misbehaving you should be able to quickly press
sorspace_barto kill the robot
- Ensure you have the Estop up and running in one terminal. Follow these instructions for e-stop
- Run keyboard teleop with this command in a new terminal
spot_keyboard_teleop
-
Before running scripts on the robot, waypoints should be recorded. These waypoints exist inside file
spot-sim2real/spot_rl_experiments/configs/waypoints.yaml -
Before recording receptacles, make the robot sit at home position then run following command
spot_reset_home
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There are 2 types of waypoints that one can record,
- clutter - These only require the (x, y, theta) of the target receptacle
- place - These requre (x, y, theta) for target receptable as well as (x, y, z) for exact drop location on the receptacle
-
To record a clutter target, teleoperate the robot to reach near the receptacle target (using joystick). Once robot is at a close distance to receptacle, run the following command
spot_rl_waypoint_recorder -c <name_for_clutter_receptacle>
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To record a place target, teleoperate the robot to reach near the receptacle target (using joystick). Once robot is at a close distance to receptacle, use manipulation mode in the joystick to manipulate the end-effector at desired (x,y,z) position. Once you are satisfied with the end-effector position, run the following command
spot_rl_waypoint_recorder -p <name_for_place_receptacle>
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In a new terminal, run the executable as
spot_rl_launch_local
This command starts 4 tmux sessions\n
- roscore
- img_publishers
- proprioception
- tts
-
You can run
tmux lsin the terminal to ensure that all 4 tmux sessions are running. You need to ensure that all 4 sessions remain active until 70 seconds after running thespot_rl_launch_local. If anyone of them dies before 70 seconds, it means there is some issue and you should rerunspot_rl_launch_local. -
You should try re-running
spot_rl_launch_localatleast 2-3 times to see if the issue still persists. Many times roscore takes a while to start due to which other nodes die, re-running can fix this issue. -
You can verify if all ros nodes are up and running as expected if the output of
rostopic listlooks like the following(spot_ros) user@linux-machine:~$ rostopic list /filtered_hand_depth /filtered_head_depth /hand_rgb /mask_rcnn_detections /mask_rcnn_visualizations /raw_hand_depth /raw_head_depth /robot_state /rosout /rosout_agg /text_to_speech -
If you don't get the output as follows, one of the tmux sessions might be failing. Follow the debugging strategies described in ISSUES.md for triaging and resolving these errors.
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This is the image visualization tool that helps to understand what robot is seeing and perceiving from the world
spot_rl_ros_img_vis
-
Running this command will open an image viewer and start printing image frequency from different rosotopics.
-
If the image frequency corresponding to
mask_rcnn_visualizationsis too large and constant (like below), it means that the bounding box detector has not been fully initialized yetraw_head_depth: 9.33 raw_hand_depth: 9.33 hand_rgb: 9.33 filtered_head_depth: 11.20 filtered_hand_depth: 11.20 mask_rcnn_visualizations: 11.20 raw_head_depth: 9.33 raw_hand_depth: 9.33 hand_rgb: 9.33 filtered_head_depth: 11.20 filtered_hand_depth: 8.57 mask_rcnn_visualizations: 11.20 raw_head_depth: 9.33 raw_hand_depth: 9.33 hand_rgb: 9.33 filtered_head_depth: 8.34 filtered_hand_depth: 8.57 mask_rcnn_visualizations: 11.20
Once the
mask_rcnn_visualizationsstart becoming dynamic (like below), you can proceed with next stepsraw_head_depth: 6.87 raw_hand_depth: 6.88 hand_rgb: 6.86 filtered_head_depth: 4.77 filtered_hand_depth: 5.01 mask_rcnn_visualizations: 6.14 raw_head_depth: 6.87 raw_hand_depth: 6.88 hand_rgb: 6.86 filtered_head_depth: 4.77 filtered_hand_depth: 5.01 mask_rcnn_visualizations: 5.33 raw_head_depth: 4.14 raw_hand_depth: 4.15 hand_rgb: 4.13 filtered_head_depth: 4.15 filtered_hand_depth: 4.12 mask_rcnn_visualizations: 4.03 raw_head_depth: 4.11 raw_hand_depth: 4.12 hand_rgb: 4.10 filtered_head_depth: 4.15 filtered_hand_depth: 4.12 mask_rcnn_visualizations: 4.03
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This is an important step. Ensure robot is at its start location and sitting, then run the following command in a new terminal
spot_reset_home
-
The waypoints that were recorded are w.r.t the home location. Since the odometry drifts while robot is moving, it is necessary to reset home before start of every new run
- Follow the steps described in e-stop section
-
Ensure you have correctly added the waypoints of interest by following the intructions to record waypoints
-
In a new terminal you can now run the code of your choice
-
To run Sequencial experts
spot_rl_mobile_manipulation_env
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To run Adaptive skill coordination
spot_rl_mobile_manipulation_env -m
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To run Language instructions with Sequencial experts, ensure the usb microphone is connected to the computer
python spot_rl_experiments/spot_rl/envs/lang_env.py
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-
If you are done with demo of one of the above code and want to run another code, you do not need to re-run other sessions and nodes. Running a new command in the same terminal will work just fine. But make sure to bring robot at home location and reset its home using
spot_reset_homein the same terminal
- Ensure FoundationPoseForSpotSim2real is setup as submodule in third_party folder please follow instructions in SETUP_INSTRUCTIONS.mds
- Currently we only support pose estimation for bottle, penguine plush toy & paper cup found in FB offices' microktichen
- New Meshes can be added using 360 video of the object from any camera (iphone, android), entire process will be described in the above repo's README
- Pose estimation model FoundationPose runs as a microservice & can be communicated through pyzmq socket
- The Step 1 should also start the pose estimation service & no other step is required to start this microservice
- How to use Pose Estimation ?
- You can pass two flags
enable_pose_estimation&enable_pose_correctionwithpickskill asskillmanager.pick(enable_pose_estimation=True, enable_pose_correction=True) - If you enable pose correction, spot will first manually correct the object pose for eg. rotate horizontal object to be vertical etc & place the corrected the object at the same place.
- Our
orientationsolvercan also correct the object to face the camera but it incurs additional pick attempt before place can be run thus is kept to be false by default - Enabling pose estimation can help in two major way - informs grasp api how to approach the object viz. topdown or side which increases the grasp success probability & correct object orientation before place is ran.
- You can pass two flags
All logs will get stored inside data/data_logs directory
The logger will capture spot's data such that each timestamp's log packet is a dict with following keys:
"timestamp" : double, # UTC epoch time from time.time()
"datatime": str # human readable corresponding local time as "YY-MM-DD HH:MM:SS"
"camera_data" : [
{
"src_info" : str, # this is name of camera source as defined in SpotCamIds
"raw_image": np.ndarray, # this is spot's camera data as cv2 (see output of Spot.image_response_to_cv2() for more info)
"camera_intrinsics": np.ndarray, # this is 3x3 matrix holding camera intrinsics
"base_T_camera": np.ndarray, # this is 4x4 transformation matrix of camera w.r.t base frame of robot
},
...
],
"vision_T_base": np.ndarray, # this is 4x4 transformation matrix of base frame w.r.t vision frame
"base_pose_xyt": np.ndarray, # this is 3 element array representing x,y,yaw w.r.t home frame
"arm_pose": np.array, # this is a 6 element array representing arm joint states (ordering : sh0, sh1, el0, el1, wr0, wr1)
"is_gripper_holding_item": bool, # whether gripper is holding something or not
"gripper_open_percentage": double, # how much is the gripper open
"gripper_force_in_hand": np.ndarray, # force estimate on end-effector in hand frameThe data logger is designed to log the data provided here at whatever rate sensor data becomes available (which depends on network setup).
To run the logger async, simply run the following command in a new terminal
python -m spot_wrapper.data_logger --log_dataThis will record data in a while loop, press Ctrl+c to spot the logger. That will save the log file inside data/data_logs/<YY,MM,DD-HH,MM,SS>.pkl file
Warning : This logger will cause motion blur as camera data is logged while the robot moves. Currently we do not support Spot-Record-Go protocol to log
It is also possible to replay the logged data (essentially the camera streams that have been logged) using the following command :
python -m spot_wrapper.data_logger --replay="<name_of_log_file>.pkl"Caution : For replay, the log file SHOULD be a pkl file with the keys provided here
Caution : Please ensure the log file is present inside data/data_logs dir.
We provide an function that can call skills in seperate conda environment. And the calling of skill itself is a non-blocking call.
python spot_rl_experiments/spot_rl/utils/skill_executor.py # In your application, you import rospy for calling which skill to use
import rospy # This is the only package you need to install in your environment
rospy.set_param("skill_name_input", "Navigate,desk") # Call navigation skills to navigate to the desk. This is a non-blocking call.Follow the steps in the project documentation.
To convert pytorch weights to torchscript, please follow Torchscript Conversion Instructions.
We thank Naoki Yokoyama for setting up the foundation of the codebase, and Joanne Truong for polishing the codebase. Spot-Sim2Real is built upon Naoki's codebases: bd_spot_wrapper and spot_rl_experiments , and with new features (LLMs, pytest) and improving robustness.
If you find this repository helpful, feel free to cite our papers: Adaptive Skill Coordination (ASC) and Language-guided Skill Coordination (LSC).
@article{yokoyama2023adaptive,
title={Adaptive Skill Coordination for Robotic Mobile Manipulation},
author={Yokoyama, Naoki and Clegg, Alexander William and Truong, Joanne and Undersander, Eric and Yang, Tsung-Yen and Arnaud, Sergio and Ha, Sehoon and Batra, Dhruv and Rai, Akshara},
journal={arXiv preprint arXiv:2304.00410},
year={2023}
}
@misc{yang2023adaptive,
title={LSC: Language-guided Skill Coordination for Open-Vocabulary Mobile Pick-and-Place},
author={Yang, Tsung-Yen and Arnaud, Sergio and Shah, Kavit and Yokoyama, Naoki and Clegg, Alexander William and Truong, Joanne and Undersander, Eric and Maksymets, Oleksandr and Ha, Sehoon and Kalakrishnan, Mrinal and Mottaghi, Roozbeh and Batra, Dhruv and Rai, Akshara},
howpublished={\url{https://languageguidedskillcoordination.github.io/}}
}
Spot-Sim2Real is MIT licensed. See the LICENSE file for details.
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