Naturalistic Robot Arm Trajectory Generation via Representation Learning
Jayjun Lee Adam J. Spiers
The integration of manipulator robots in household environments suggests a
need for more predictable and human-like robot motion. This holds especially
true for wheelchair-mounted assistive robots that can support the independence
of people with paralysis. One method of generating naturalistic motion
trajectories is via the imitation of human demonstrators. This paper explores a
self-supervised imitation learning method using an autoregressive
spatio-temporal graph neural network for an assistive drinking task. We address
learning from diverse human motion trajectory data that were captured via
wearable IMU sensors on a human arm as the action-free task demonstrations.
Observed arm motion data from several participants is used to generate natural
and functional drinking motion trajectories for a UR5e robot arm.