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Scripts for real-robot deployment

Overview

Our real-world experimental setup looks as below:

real-world-setup

We mount 3x Realsense D435 cameras around the Franka Emika Panda robot arm, on which the gripper is wrapped with a rubber glove.

To facilitate our heuristic for color-based segmentation, we place a green mat over the robot workspace in range x=[0.25m,0.75m] and y=[-0.3m,+0.3m] in the robot frame.

Package Setup

Refer again to the Dockerfile for our environment and package dependencies.

For real-world deployment, we additionally require polymetis, which can be installed by following the instructions in the linked page.

Calibration

Calibrate the cameras and robot extrinsics, which generates the following files in the ${PKM_RUNTIME_DIR} directory:

$ python3 collect_calibration_data.py # -> ${PKM_RUNTIME_DIR}/pq_list.pkl
$ python3 calibrate_transforms.py # -> ${PKM_RUNTIME_DIR}/transforms.pkl
$ python3 multi_cam_calibration.py # -> ${PKM_RUNTIME_DIR}/extrinsics.pkl

Calibrate color-based segmentation algorithm parameters

$ python3 tune_hsv_threshold.py

This generates the ${PKM_RUNTIME_DIR}/table_color.pkl, which is required for color-based background segmentation.

Calibrate object-specific april tag tracker

Our perception stack optionally utilizes april-tag based object tracking system.

If you would like to use this feature, then calibrate the object-specific april tag tracker as follows:

$ python3 print_april_tag_ids.py object=${OBJECT} tag_size=${TAG_SIZE}
$ python3 calibrate_multicam_april_tag_tracker.py object=${OBJECT} tag_size=${TAG_SIZE}

Here, ${OBJECT} can be any unique id that you assign for a particular object, and tag_size is the size of the (default) 36h11 april-tag that has been pasted on the object.

Experiment Setup

Task configuration generation

To evaluate the policies, sample a set of goal poses in the real-world:

$ python3 sample_goal_pose.py # -> ${PKM_RUNTIME_DIR}/task{index:04d}.pkl

Policy Deployment

Running the policy requires the following files (generated in the above steps)

  • ${PKM_RUNTIME_DIR}/extrinsics.pkl
  • ${PKM_RUNTIME_DIR}/task*.pkl

By default, the latest transforms/extrinsics/task files are selected.

After the above configuration steps have been completed, execute the policy by running the controller:

$ python3 controller.py -ckpt ${STUDENT_CKPT} -dagger -obj ${OBJ} -april 0

where ${STUDENT_CKPT} should be replaced with the result from the distillation pipeline, and the ${OBJ} refers to the object to test. If you have already calibrated your object in the above step, you may also want to toggle -april 1 instead.

Extras & Tools

We offer a set of additional tools to work with the real world experiments.

Test perception stack

To run the perception stack independently, without running the controller:

$ python3 test_perception.py

Replay logs

To see the saved logs from a previous execution, run:

$ python3 replay_log.py path="${LOG_PATH}"

To test controlling the franka panda robot with a joystick (e.g. to test the connection):

$ python3 joystick_controller.py