Robot Trajectron: Trajectory Prediction-based Shared Control for Robot
  Manipulation
Pinhao Song Pengteng Li Erwin Aertbelien Renaud Detry
We address the problem of (a) predicting the trajectory of an arm reaching
motion, based on a few seconds of the motion's onset, and (b) leveraging this
predictor to facilitate shared-control manipulation tasks, easing the cognitive
load of the operator by assisting them in their anticipated direction of
motion. Our novel intent estimator, dubbed the \emph{Robot Trajectron} (RT),
produces a probabilistic representation of the robot's anticipated trajectory
based on its recent position, velocity and acceleration history. Taking arm
dynamics into account allows RT to capture the operator's intent better than
other SOTA models that only use the arm's position, making it particularly
well-suited to assist in tasks where the operator's intent is susceptible to
change. We derive a novel shared-control solution that combines RT's predictive
capacity to a representation of the locations of potential reaching targets.
Our experiments demonstrate RT's effectiveness in both intent estimation and
shared-control tasks. We will make the code and data supporting our experiments
publicly available at https://github.com/mousecpn/Robot-Trajectron.git.