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Active Learning Experiment Workflow

By Izzy Brand, Sebastian Castro, Caris Moses, Michael Noseworthy

Overview

Machines:

  • Panda + control box -- the robot
  • RTK -- runs Panda agent, robot interface, and vision
  • Desktop machine -- runs planning server and active learning

RTK Setup

Connecting to the robot

Starting the Franka interface

cd ~/catkin_ws
./franka.sh master
roslaunch franka_interface interface.launch

Interactive Python console to reset the robot position/error state

roscd franka_interface/scripts
python3.7 -i interactive.py
>> arm.move_to_neutral()
>> arm.resetErrors()

Starting vision server

taskset --cpu-list 10,11 roslaunch panda_vision vision.launch

Starting the Panda agent server

taskset --cpu-list 4,5,8,9 rosrun stacking_ros panda_agent_server.py --num-blocks 10 --real --use-vision

Desktop Setup

Starting the task and motion planning server

python stacking_ros/scripts/planning_server.py --blocks-file learning/domains/towers/final_block_set_10.pkl --use-vision --alternate-orientations --num-blocks 10 --max-tries 2

Active learning (starting from scratch)

python -m learning.experiments.active_train_towers --exec-mode real --use-panda-server --block-set-fname learning/domains/towers/final_block_set_10.pkl --n-epochs 20 --n-acquire 10 --sampler sequential --exp-name robot-seq-init-sim --n-samples 100000

Active learning (restarting from existing results)

python -m learning.experiments.restart_active_train_towers --exp-path learning/experiments/logs/exp-20210218-161131

To evaluate current progress, run this with the appropriate arguments (tx).

python -m learning.evaluate.plot_model_accuracy --exp-paths learning/experiments/logs/robot-seq-init-sim-20210219-131924 --max-acquisitions <tx> --plot-step 1 --test-set-fname learning/evaluate/test_datasets/eval_blocks_test_dataset.pkl --output-fname test

If a tower is mislabeled during acquisition step <tx>, run the following AFTER all towers for step <tx> have been labeled, and acquired_<tx>.pkl has been generated. <tn> corresponds to the tower number integer in the towers file name (0 indexed). <l> is the correct label. If any files from step <tx>+1 have been generated then delete them. (They will be regenerated/trained on restart)

python -m learning.experiments.fix_tower_label --exp-path <path> --acquisition-step <tx>
--tower-number <tn> --label <l>

Video collection laptop setup

FFMpeg magic command