  This work proposed an efficient learning-based framework to learn feedback
control policies from human teleoperated demonstrations, which achieved
obstacle negotiation, staircase traversal, slipping control and parcel delivery
for a tracked robot. Due to uncertainties in real-world scenarios, eg obstacle
and slippage, closed-loop feedback control plays an important role in improving
robustness and resilience, but the control laws are difficult to program
manually for achieving autonomous behaviours. We formulated an architecture
based on a long-short-term-memory (LSTM) neural network, which effectively
learn reactive control policies from human demonstrations. Using datasets from
a few real demonstrations, our algorithm can directly learn successful
policies, including obstacle-negotiation, stair-climbing and delivery, fall
recovery and corrective control of slippage. We proposed decomposition of
complex robot actions to reduce the difficulty of learning the long-term
dependencies. Furthermore, we proposed a method to efficiently handle
non-optimal demos and to learn new skills, since collecting enough
demonstration can be time-consuming and sometimes very difficult on a real
robotic system.
