  Deep imitation learning enables robots to learn from expert demonstrations to
perform tasks such as lane following or obstacle avoidance. However, in the
traditional imitation learning framework, one model only learns one task, and
thus it lacks of the capability to support a robot to perform various different
navigation tasks with one model in indoor environments. This paper proposes a
new framework, Shared Multi-headed Imitation Learning(SMIL), that allows a
robot to perform multiple tasks with one model without switching among
different models. We model each task as a sub-policy and design a multi-headed
policy to learn the shared information among related tasks by summing up
activations from all sub-policies. Compared to single or non-shared
multi-headed policies, this framework is able to leverage correlated
information among tasks to increase performance.We have implemented this
framework using a robot based on NVIDIA TX2 and performed extensive experiments
in indoor environments with different baseline solutions. The results
demonstrate that SMIL has doubled the performance over nonshared multi-headed
policy.
