Video presentation of our paper "TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space" at IEEE ICRA 2020.
We present TrueRMA, a data-efficient, model-
free method to learn cost-optimized robot trajectories over a
wide range of starting points and endpoints. The key idea
is to calculate trajectory waypoints in Cartesian space by
recursively predicting orthogonal adaptations relative to the
midpoints of straight lines. We generate a differentiable path
by adding circular blends around the waypoints, calculate the
corresponding joint positions with an inverse kinematics solver
and calculate a time-optimal parameterization considering ve-
locity and acceleration limits. During training, the trajectory is
executed in a physics simulator and costs are assigned according
to a user-specified cost function which is not required to be
differentiable. Given a starting point and an endpoint as input,
a neural network is trained to predict midpoint adaptations that
minimize the cost of the resulting trajectory via reinforcement
learning. We successfully train a KUKA iiwa robot to keep
a ball on a plate while moving between specified points and
compare the performance of TrueRMA against two baselines.
The results show that our method requires less training data to
learn the task while generating shorter and faster trajectories.
Jonas C. Kiemel, Pascal Meißner and Torsten Kröger