  This paper presents a vision-based learning-by-demonstration approach to
enable robots to learn and complete a manipulation task cooperatively. With
this method, a vision system is involved in both the task demonstration and
reproduction stages. An expert first demonstrates how to use tools to perform a
task, while the tool motion is observed using a vision system. The
demonstrations are then encoded using a statistical model to generate a
reference motion trajectory. Equipped with the same tools and the learned
model, the robot is guided by vision to reproduce the task. The task
performance was evaluated in terms of both accuracy and speed. However, simply
increasing the robot's speed could decrease the reproduction accuracy. To this
end, a dual-rate Kalman filter is employed to compensate for latency between
the robot and vision system. More importantly, the sampling rates of the
reference trajectory and the robot speed are optimised adaptively according to
the learned motion model. We demonstrate the effectiveness of our approach by
performing two tasks: a trajectory reproduction task and a bimanual sewing
task. We show that using our vision-based approach, the robots can conduct
effective learning by demonstrations and perform accurate and fast task
reproduction. The proposed approach is generalisable to other manipulation
tasks, where bimanual or multi-robot cooperation is required.
