Control, planning, and learning system for physical human-robot interaction (pHRI) with a JACO2 7DOF robotic arm.

• Ubuntu 14.04, ROS Indigo, OpenRAVE, Python 2.7
• or_trajopt, or_urdf, or_rviz, prpy, pr_ordata
• kinova-ros
• fcl

Turn the robot on and put it in home position by pressing and holding the center (yellow) button on the joystick.

In a new terminal, turn on the Kinova API by typing:

roslaunch kinova_bringup kinova_robot.launch kinova_robotType:=j2s7s300 use_urdf:=true

In another terminal, run:

roslaunch iact_control trajoptPID.launch ID:=0 task:=0 methodType:=A demo:=F record:=F

Command-line options include:

• ID: Participant/user identification number (for experiments and data saving)
• task: Task number {Distance to human = 0, Cup orientation = 1, Distance to table = 2, Distance to laptop = 3}
• methodType: Sets the pHRI control method {impedance control = A, impedance + learning from pHRI = B}
• demo: Demonstrates the "optimal" way to perform the task {default = F, optimal demo = T}
• record: Records the interaction forces, measured trajectories, and cost function weights for a task {record data = T, don't record = F}

Code used in the following papers:

• A. Bajcsy* , D.P. Losey*, M.K. O'Malley, and A.D. Dragan. Learning Robot Objectives from Physical Human Robot Interaction. Conference on Robot Learning (CoRL), 2017.
• A. Bajcsy , D.P. Losey, M.K. O'Malley, and A.D. Dragan. Learning from Physical Human Corrections, One Feature at a Time. International Confernece on Human-Robot Interaction (HRI), 2018.

• TrajOpt Planner: http://rll.berkeley.edu/trajopt/doc/sphinx_build/html/index.html
• PID Control Reference: https://w3.cs.jmu.edu/spragunr/CS354/handouts/pid.pdf