Manipulator Gym

This package provides a common gym-like environment for policy to interact with a manipulator robot. The environment is based on the gym interface, and the robots are defined as interfaces. The abstracted robot interfaces is easily swappable, modularized and run distributedly. Common utilities are provided to assist users to run robot policies (e.g. octo) on the robot.

Manipulator Interfaces	Description
widowx	Interbotix widowx interface in ROS1
widowx_ros2	Interbotix widowx interface in ROS2
widowx_sim	Interbotix widowx in a pybullet sim
viperx	Interbotix viperx interface in ROS1
service_client	client interface to connect a remote interface server

more coming soon.... (etc. mujoco panda)

Other useful features are also provided

• Eval script to run a robot policy octo and openvla
• Data collection script to collect data for RLDS training (also support remote data collection)
• Fine-tuning script to finetune the robot policy with the collected RLDS data

Installations:

• agentlace: for distributed robot agent interfaces
• pybullet: used in widowx_sim. (Optional)
• oxe_envlogger: for logging the data in RLDS format (Optional)
• Robot Policies (Optional):
  • octo
  • openvla

Quick Start

Code Snippets

To use the gym env, is quite straightforward. Define it as such:

from manipulator_gym.manipulator_env import ManipulatorEnv
from manipulator_gym.interfaces.base_interface import ManipulatorInterface

# 1. define select the robot interface
# ManipulatorInterface is an abstract class, you choose the interfaces from the list above
# e.g. interface = WidowXSimInterface(), ViperXInterface(), etc.
interface = ManipulatorInterface()

# 2. define the gym env, Done!
env = ManipulatorEnv(interface=interface)

# 3. Use the 'env' as standard gym env. E.g.: env.reset(), env.step(), etc.

Then, you can use the gym env as you would with any other gym env. 🥳 You can also use other interfaces to run different kinds of manipulators, e.g. widowx, viperx, action_client-server, etc.

Also, we can make the interface to be a server-client setup, to run the policy on a different process, or even machine. Create 2 seperate scripts server.py and client.py:

1. In server.py. Run the interface as server

from manipulator_gym.interfaces.base_interface import ManipulatorInterface
from manipulator_gym.interfaces.interface_service import ManipulatorInterfaceServer

server = ManipulatorInterfaceServer(manipulator_interface=interface)
server.start()

2. In client.py. Then run the env with the client interface

from manipulator_gym.manipulator_env import ManipulatorEnv
from manipulator_gym.interfaces.interface_service import ActionClientInterface

interface = ActionClientInterface(host=FLAGS.ip)
env = ManipulatorEnv(manipulator_interface=interface)

# Use the 'env' as standard gym env. E.g.: env.reset(), env.step(), etc.

Tada!

Run Examples

1. Run a scripted rollout on the robot

How to run a simple sim env of a widowx robot, while enabling logging of rlds data.

# This runs a sim env of the manipulator
# To logs the data in RLDS format, add: --log_dir log_dir/   (requires oxe_envlogger)
python manipulator_gym/manipulator_env.py --widowx_sim

To visualize logged data

python read_rlds.py --show_img --rlds_dir log_dir/

2. Robot Teleoperation

Run the "Robot Server" with a simulated widowx robot.

python manipulator_server.py --widowx_sim

Run Teleoperation script (action client) to control the robot with keyboard. Provide IP for remote teleoperation.

python teleop.py # --ip IP

**Provide --use_spacemouse to use the spacemouse for teleoperation

3. Run a Robot policy

Now, we will replace the teleop.py with a robot policy.

Run the "Robot Server"

python manipulator_server.py --widowx_sim

Run the "Policy Client":

We provide 2 example generalist policies to evaluate a manipulation task with text conditioning:

• Octo: octo

# --text_cond is the task condition, e.g. "put the red marker on the table"
python policies/octo_eval.py --ip IP_ADDRESS --show_img --text_cond "put the banana on the plate"

• OpenVLA: openvla

# --text_cond is the task condition, e.g. "put the red marker on the table"
python policies/vla_eval.py --ip IP_ADDRESS --show_img --text_cond "put the banana on the plate"

Communication nodes looks like this:

graph LR
    A[Widowx Sim, server interface]
    A <--agentlace--> B[Gym Env, action client interface <-> Octo Policy]

Loading

☝️ This is what you expect to see when running the above commands.

However, you would expect that the generalist policy would not work well in the simulation environment as it is not trained on the simulation data.

---

Interfaces

WidowX Sim

This uses the pybullet sim to simulate the widowx robot.

pip install pybullet

ViperX or WidowX

This requires user to download the respective dependencies for the robot, and run the robot interface.

Install the interbotix_ros_arms package for the viper/widowx robot for follow the docs from trossen roboitcs:

• ros: link
• ros2: link

Now, it is time to run the policy on the real robot. To enable this, we use the following architecture to abstract out the robot env with the policy. This is enabled by agentlace.

graph LR
    A[Robot Driver] <--ROS Topics--> B[Manipulation server]
    B <--agentlace--> C[Gym Env <-> Octo Policy]

Loading

1. Run the robot driver (Note the ros1 and ros2 launch commands)

# ros1:
roslaunch interbotix_xsarm_control xsarm_control.launch robot_model:=wx250s use_rviz:=false
# ros2 example:
ros2 launch interbotix_xsarm_control xsarm_control.launch.py robot_model:=wx250s

2. Run the widowx/viperx server interface

# choose viperx

# ros1 [cam_ids is the USB camera id]
python3 manipulator_server.py --widowx --cam_ids 0

# ros2 [cam_ids is the USB camera id]
python manipulator_server.py --widowx_ros2 --cam_ids 0

3. Run the octo model on a different machine

python policies/octo_eval.py --ip IP_ADDRESS --show_img --text_cond "PROVIDE YOUR TEXT TASK"

---

Finetuning pipeline

Data collection and Fine-tuning of Octo model

Collect expert demonstations via teleop.

# Usage
# --log_dir to log the data in RLDS format, requires oxe_envlogger
# --reset_pose <x y z r p y gripper> to reset the robot to a specific pose
# --log_lang_text is an optional str to provide a text for language conditioned task
python manipulator_gym/teleop.py --ip <IP_ADDRESS> --log_dir <LOG_DIR> --log_lang_text "pick up the something to something"

Data is saved as a trajectory per shard. When click on r, this will reset the robot and start a new trajectory. When click on q, this will stop the teleop and save the data.

(optional) Validate the generated log files by replaying on the robot gym env

python3 read_rlds.py --show_img --rlds_dir PATH_TO_LOGS  --replay

Octo Finetuning

Now finetune the model using the generated log files

cd octo
python scripts/finetune.py --config=../manipulator_gym/viperx_finetune_config.py --config.pretrained_path=hf://rail-berkeley/octo-small

Lastly,, evaluate the finetuned model with the same octo_eval.py script as above. (provide the new chkpt and dataset stats)

OpenVLA Finetuning

We can also use the collected RLDS for OpenVLA finetuning, check out the doc in openvla for more details. Reference to this PR: openvla/openvla#86.

Create a such directory structure with the expert demos collected via teleop.py. The fine-tuned adapted checkpoints of VLA will be saved in vla_storage/checkpoints

~/vla_storage
 |_ checkpoints             # full merged model checkpoints
 |_ adapter_checkpoints     # adapter checkpoints
 |_ expert_demos
    |_ 0.1.0
       |_ dataset_info.json
       |_ features.json
       |_ expert_demos-train.tfrecord....

# we will use Single Node of 2 GPUs to finetune the model
# adjust the args accordingly
torchrun --standalone --nnodes 1 --nproc-per-node 2 vla-scripts/finetune.py --batch_size 4 --shuffle_buffer_size 10000 --lora_rank 32 \
--data_root_dir ~/vla_storage --dataset_name expert_demos --run_root_dir ~/vla_storage/checkpoints --adapter_tmp_dir  ~/vla_storage/adapter_checkpoints--use_quantization true --save_steps 1000 \
--wandb_project <PROJECT_NAME> --wandb_entity <YOUR_WANDB_ACCOUNT>

Evaluate the finetuned model

python policies/vla_eval.py --ip 128.32.175.45 --show_img --text_cond "move the eggplant from the basket to the center of the sink" --lora_adapter_dir <PATH_TO_ADAPTER_CHECKPOINT>
--dataset_stats <PATH_TO_DATASET_STATS>

---

Others

FAQ

1. Interbotix ROS Installation Error.

CMake Error at /opt/ros/noetic/share/catkin/cmake/empy.cmake:30 (message):
  Unable to find either executable 'empy' or Python module 'em'...  try
  installing the package 'python3-empy'
Call Stack (most recent call first):
  /opt/ros/noetic/share/catkin/cmake/all.cmake:164 (include)
  /opt/ros/noetic/share/catkin/cmake/catkinConfig.cmake:20 (include)
  CMakeLists.txt:58 (find_package)


-- Configuring incomplete, errors occurred!
See also "/home/pranav/interbotix_ws/build/CMakeFiles/CMakeOutput.log".
Invoking "cmake" failed
[ERROR] Failed to build Interbotix Arm ROS Packages.
[ERROR] Interbotix Installation Failed!

Make sure that you are not running the installation in a conda environment. If you are, deactivate the conda environment and run the installation again.

Notes

• If you wish to directly wrap the gym env for distributed gym inference, you can directly use agentlace's action env wrapper
• If you wish to save the raw teleop data in pkl format, indicate --log_type pkl when running the teleop.py script
• This is still in active development. Open issues for wishlist and bugs.
• If you wish to add new robot interface and new control method, it should be easy by adding new interface in manipulator_gym/interfaces. Feel free to open a PR for review.
• For remote internet teleop, some useful network tools to use:
  • Setup a simple VPN service with tailscale. Connect with local VPN ip
  • Setup TCP Tunnel with ngrok. run grok tcp 5556 on server, and client use the tcp ip and port.
• Potential additions and improvements:
  • More interfaces (e.g. mujoco panda, franka, etc.)
  • More sim envs (e.g. robosuite etc.)
  • create util scripts for wandb eval loggings
  • better interfaces with extend-able sensors and actuators (e.g. camera, bimanual manipulators, etc.)
  • Cleaner impl of VR controller method with oculus_reader to collect data with occulus vr controller (linear movement with controller, rotation with controller joystick)*