  Robot gait optimization is the task of generating an optimal control
trajectory under various internal and external constraints. Given the high
dimensions of control space, this problem is particularly challenging for
multi-legged robots walking in complex and unknown environments. Existing
literatures often regard the gait generation as an optimization problem and
solve the gait optimization from scratch for robots walking in a specific
environment. However, such approaches do not consider the use of pre-acquired
knowledge which can be useful in improving the quality and speed of motion
generation in complex environments. To address the issue, this paper proposes a
transfer learning-based evolutionary framework for multi-objective gait
optimization, named Tr-GO. The idea is to initialize a high-quality population
by using the technique of transfer learning, so any kind of population-based
optimization algorithms can be seamlessly integrated into this framework. The
advantage is that the generated gait can not only dynamically adapt to
different environments and tasks, but also simultaneously satisfy multiple
design specifications (e.g., speed, stability). The experimental results show
the effectiveness of the proposed framework for the gait optimization problem
based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and
MOPSO. When transferring the pre-acquired knowledge from the plain terrain to
various inclined and rugged ones, the proposed Tr-GO framework accelerates the
evolution process by a minimum of 3-4 times compared with non-transferred
scenarios.
