  Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics.
