Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space

TL;DR

This paper presents a method for autonomous trajectory learning of space robot arms using imitation learning with probabilistic movement primitives, enabling efficient, adaptable, and safe operations in space without extensive real hardware testing.

Contribution

It introduces a novel approach combining MPC-generated demonstration data with ProMPs for trajectory learning, tailored for space robot manipulators with redundant DoF.

Findings

ProMPs enable generalization to unseen situations.

Redundant DoF allows multiple trajectory options.

Trajectory sampling reduces attitude disturbances.

Abstract

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.