When a Robot is More Capable than a Human: Learning from Constrained Demonstrators

ArXi:2510.09096v2 Announce Type: replace-cross Learning from nstrations enables experts to teach robots complex tasks using interfaces such as kinesthetic teaching, joystick control, and sim-to-real transfer. However, these interfaces often constrain the expert's ability to nstrate optimal behavior due to indirect control, setup restrictions, and hardware safety. For example, a joystick can move a robotic arm only in a 2D plane, even though the robot operates in a higher-dimensional space.