  This work adds on to the on-going efforts to provide more autonomy to space
robots. Here the concept of programming by demonstration or imitation learning
is used for trajectory planning of manipulators mounted on small spacecraft.
For greater autonomy in future space missions and minimal human intervention
through ground control, a robot arm having 7-Degrees of Freedom (DoF) is
envisaged for carrying out multiple tasks like debris removal, on-orbit
servicing and assembly. Since actual hardware implementation of microgravity
environment is extremely expensive, the demonstration data for trajectory
learning is generated using a model predictive controller (MPC) in a physics
based simulator. The data is then encoded compactly by Probabilistic Movement
Primitives (ProMPs). This offline trajectory learning allows faster
reproductions and also avoids any computationally expensive optimizations after
deployment in a space environment. It is shown that the probabilistic
distribution can be used to generate trajectories to previously unseen
situations by conditioning the distribution. The motion of the robot (or
manipulator) arm induces reaction forces on the spacecraft hub and hence its
attitude changes prompting the Attitude Determination and Control System (ADCS)
to take large corrective action that drains energy out of the system. By having
a robot arm with redundant DoF helps in finding several possible trajectories
from the same start to the same target. This allows the ProMP trajectory
generator to sample out the trajectory which is obstacle free as well as having
minimal attitudinal disturbances thereby reducing the load on ADCS.

Robotics;Artificial Intelligence;Machine Learning